cdocs-build:
image: "klakegg/hugo:ext-alpine"
depends_on:
- - "init"
+ - "cdocs-init"
volumes:
- "../docs:/src"
- "../csit.infra.dash:/cdash"
image: "klakegg/hugo:ext-alpine"
command: "server --minify"
depends_on:
- - "init"
+ - "cdocs-init"
volumes:
- "../docs:/src"
ports:
---
+bookCollapseSection: true
bookFlatSection: true
title: "Methodology"
weight: 2
--- /dev/null
+---
+bookToc: false
+title: "Access Control Lists"
+weight: 12
+---
+
+# Access Control Lists
+
+VPP is tested in a number of data plane feature configurations across
+different forwarding modes. Following sections list features tested.
+
+## ACL Security-Groups
+
+Both stateless and stateful access control lists (ACL), also known as
+security-groups, are supported by VPP.
+
+Following ACL configurations are tested for MAC switching with L2
+bridge-domains:
+
+- *l2bdbasemaclrn-iacl{E}sl-{F}flows*: Input stateless ACL, with {E}
+ entries and {F} flows.
+- *l2bdbasemaclrn-oacl{E}sl-{F}flows*: Output stateless ACL, with {E}
+ entries and {F} flows.
+- *l2bdbasemaclrn-iacl{E}sf-{F}flows*: Input stateful ACL, with {E}
+ entries and {F} flows.
+- *l2bdbasemaclrn-oacl{E}sf-{F}flows*: Output stateful ACL, with {E}
+ entries and {F} flows.
+
+Following ACL configurations are tested with IPv4 routing:
+
+- *ip4base-iacl{E}sl-{F}flows*: Input stateless ACL, with {E} entries
+ and {F} flows.
+- *ip4base-oacl{E}sl-{F}flows*: Output stateless ACL, with {E} entries
+ and {F} flows.
+- *ip4base-iacl{E}sf-{F}flows*: Input stateful ACL, with {E} entries and
+ {F} flows.
+- *ip4base-oacl{E}sf-{F}flows*: Output stateful ACL, with {E} entries
+ and {F} flows.
+
+ACL tests are executed with the following combinations of ACL entries
+and number of flows:
+
+- ACL entry definitions
+
+ - flow non-matching deny entry: (src-ip4, dst-ip4, src-port, dst-port).
+ - flow matching permit ACL entry: (src-ip4, dst-ip4).
+
+- {E} - number of non-matching deny ACL entries, {E} = [1, 10, 50].
+- {F} - number of UDP flows with different tuple (src-ip4, dst-ip4,
+ src-port, dst-port), {F} = [100, 10k, 100k].
+- All {E}x{F} combinations are tested per ACL type, total of 9.
+
+## ACL MAC-IP
+
+MAC-IP binding ACLs are tested for MAC switching with L2 bridge-domains:
+
+- *l2bdbasemaclrn-macip-iacl{E}sl-{F}flows*: Input stateless ACL, with
+ {E} entries and {F} flows.
+
+MAC-IP ACL tests are executed with the following combinations of ACL
+entries and number of flows:
+
+- ACL entry definitions
+
+ - flow non-matching deny entry: (dst-ip4, dst-mac, bit-mask)
+ - flow matching permit ACL entry: (dst-ip4, dst-mac, bit-mask)
+
+- {E} - number of non-matching deny ACL entries, {E} = [1, 10, 50]
+- {F} - number of UDP flows with different tuple (dst-ip4, dst-mac),
+ {F} = [100, 10k, 100k]
+- All {E}x{F} combinations are tested per ACL type, total of 9.
--- /dev/null
+---
+bookCollapseSection: true
+bookFlatSection: false
+title: "Data Plane Throughput"
+weight: 4
+---
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "Data Plane Throughput"
+weight: 1
+---
+
+# Data Plane Throughput
+
+Network data plane throughput is measured using multiple test methods in
+order to obtain representative and repeatable results across the large
+set of performance test cases implemented and executed within CSIT.
+
+Following throughput test methods are used:
+
+- MLRsearch - Multiple Loss Ratio search
+- MRR - Maximum Receive Rate
+- PLRsearch - Probabilistic Loss Ratio search
+
+Description of each test method is followed by generic test properties
+shared by all methods.
+
+## MLRsearch Tests
+
+### Description
+
+Multiple Loss Ratio search (MLRsearch) tests discover multiple packet
+throughput rates in a single search, reducing the overall test execution
+time compared to a binary search. Each rate is associated with a
+distinct Packet Loss Ratio (PLR) criteria. In FD.io CSIT two throughput
+rates are discovered: Non-Drop Rate (NDR, with zero packet loss, PLR=0)
+and Partial Drop Rate (PDR, with PLR<0.5%). MLRsearch is compliant with
+RFC2544.
+
+### Usage
+
+MLRsearch tests are run to discover NDR and PDR rates for each VPP and
+DPDK release covered by CSIT report. Results for small frame sizes
+(64b/78B, IMIX) are presented in packet throughput graphs
+(Box-and-Whisker Plots) with NDR and PDR rates plotted against the test
+cases covering popular VPP packet paths.
+
+Each test is executed at least 10 times to verify measurements
+repeatability and results are compared between releases and test
+environments. NDR and PDR packet and bandwidth throughput results for
+all frame sizes and for all tests are presented in detailed results
+tables.
+
+### Details
+
+See [MLRSearch]({{< ref "mlrsearch/#MLRsearch" >}}) section for more detail.
+MLRsearch is being standardized in IETF in
+[draft-ietf-bmwg-mlrsearch](https://datatracker.ietf.org/doc/html/draft-ietf-bmwg-mlrsearch-01).
+
+## MRR Tests
+
+### Description
+
+Maximum Receive Rate (MRR) tests are complementary to MLRsearch tests,
+as they provide a maximum “raw” throughput benchmark for development and
+testing community.
+
+MRR tests measure the packet forwarding rate under the maximum load
+offered by traffic generator (dependent on link type and NIC model) over
+a set trial duration, regardless of packet loss. Maximum load for
+specified Ethernet frame size is set to the bi-directional link rate.
+
+### Usage
+
+MRR tests are much faster than MLRsearch as they rely on a single trial
+or a small set of trials with very short duration. It is this property
+that makes them suitable for continuous execution in daily performance
+trending jobs enabling detection of performance anomalies (regressions,
+progressions) resulting from data plane code changes.
+
+MRR tests are also used for VPP per patch performance jobs verifying
+patch performance vs parent. CSIT reports include MRR throughput
+comparisons between releases and test environments. Small frame sizes
+only (64b/78B, IMIX).
+
+### Details
+
+See [MRR Throughput]({{< ref "mrr_throughput/#MRR Throughput" >}})
+section for more detail about MRR tests configuration.
+
+FD.io CSIT performance dashboard includes complete description of
+[daily performance trending tests](https://s3-docs.fd.io/csit/master/trending/methodology/performance_tests.html)
+and [VPP per patch tests](https://s3-docs.fd.io/csit/master/trending/methodology/perpatch_performance_tests.html).
+
+## PLRsearch Tests
+
+### Description
+
+Probabilistic Loss Ratio search (PLRsearch) tests discovers a packet
+throughput rate associated with configured Packet Loss Ratio (PLR)
+criteria for tests run over an extended period of time a.k.a. soak
+testing. PLRsearch assumes that system under test is probabilistic in
+nature, and not deterministic.
+
+### Usage
+
+PLRsearch are run to discover a sustained throughput for PLR=10^-7
+(close to NDR) for VPP release covered by CSIT report. Results for small
+frame sizes (64b/78B) are presented in packet throughput graphs (Box
+Plots) for a small subset of baseline tests.
+
+Each soak test lasts 30 minutes and is executed at least twice. Results are
+compared against NDR and PDR rates discovered with MLRsearch.
+
+### Details
+
+See [PLRSearch]({{< ref "plrsearch/#PLRsearch" >}}) methodology section for
+more detail. PLRsearch is being standardized in IETF in
+[draft-vpolak-bmwg-plrsearch](https://tools.ietf.org/html/draft-vpolak-bmwg-plrsearch).
+
+## Generic Test Properties
+
+All data plane throughput test methodologies share following generic
+properties:
+
+- Tested L2 frame sizes (untagged Ethernet):
+
+ - IPv4 payload: 64B, IMIX (28x64B, 16x570B, 4x1518B), 1518B, 9000B.
+ - IPv6 payload: 78B, IMIX (28x78B, 16x570B, 4x1518B), 1518B, 9000B.
+ - All quoted sizes include frame CRC, but exclude per frame
+ transmission overhead of 20B (preamble, inter frame gap).
+
+- Offered packet load is always bi-directional and symmetric.
+- All measured and reported packet and bandwidth rates are aggregate
+ bi-directional rates reported from external Traffic Generator
+ perspective.
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "MLRsearch"
+weight: 2
+---
+
+# MLRsearch
+
+## Overview
+
+Multiple Loss Ratio search (MLRsearch) tests use an optimized search algorithm
+implemented in FD.io CSIT project. MLRsearch discovers any number of
+loss ratio loads in a single search.
+
+Two loss ratio goals are of interest in FD.io CSIT, leading to Non-Drop Rate
+(NDR, loss ratio goal is exact zero) and Partial Drop Rate
+(PDR, non-zero loss ratio goal, currently 0.5%).
+
+MLRsearch discovers all the loads in a single pass, reducing required time
+duration compared to separate `binary search`es[^1] for each rate. Overall
+search time is reduced even further by relying on shorter trial
+durations of intermediate steps, with only the final measurements
+conducted at the specified final trial duration. This results in the
+shorter overall execution time when compared to standard NDR/PDR binary
+search, while guaranteeing similar results.
+
+.. Note:: All throughput rates are *always* bi-directional
+ aggregates of two equal (symmetric) uni-directional packet rates
+ received and reported by an external traffic generator,
+ unless the test specifically requires unidirectional traffic.
+
+## Search Implementation
+
+Detailed description of the MLRsearch algorithm is included in the IETF
+draft
+[draft-ietf-bmwg-mlrsearch-02](https://datatracker.ietf.org/doc/html/draft-ietf-bmwg-mlrsearch-02)
+that is in the process of being standardized in the IETF Benchmarking
+Methodology Working Group (BMWG).
+(Newer version is published in IETF, describing improvements not yet used
+in CSIT production.)
+
+MLRsearch is also available as a
+[PyPI (Python Package Index) library](https://pypi.org/project/MLRsearch/).
+
+## Algorithm highlights
+
+MRR and receive rate at MRR load are used as initial guesses for the search.
+
+All previously measured trials (except the very first one which can act
+as a warm-up) are taken into consideration, unless superseded
+by a trial at the same load but higher duration.
+
+For every loss ratio goal, tightest upper and lower bound
+(from results of large enough trial duration) form an interval.
+Exit condition is given by that interval reaching low enough relative width.
+Small enough width is achieved by bisecting the current interval.
+The bisection can be uneven, to save measurements based on information theory.
+
+Switching to higher trial duration generally requires a re-measure
+at a load from previous trial duration.
+When the re-measurement does not confirm previous bound classification
+(e.g. tightest lower bound at shorter trial duration becomes
+a newest tightest upper bound upon re-measurement),
+external search is used to find close enough bound of the lost type.
+External search is a generalization of the first stage of
+`exponential search`[^2].
+
+Shorter trial durations use double width goal,
+because one bisection is always safe before risking external search.
+
+Within an iteration for a specific trial duration, smaller loss ratios (NDR)
+are narrowed down first before search continues with higher loss ratios (PDR).
+
+Other heuristics are there, aimed to prevent unneccessarily narrow intervals,
+and to handle corner cases around min and max load.
+
+## Deviations from RFC 2544
+
+CSIT does not have any explicit wait times before and after trial traffic.
+
+Small differences between intended and offered load are tolerated,
+mainly due to various time overheads preventing precise measurement
+of the traffic duration (and TRex can sometimes suffer from duration
+stretching).
+
+The final trial duration is only 30s (10s for reconf tests).
+
+[^1]: [binary search](https://en.wikipedia.org/wiki/Binary_search)
+[^2]: [exponential search](https://en.wikipedia.org/wiki/Exponential_search)
--- /dev/null
+---
+bookToc: false
+title: "MRR Throughput"
+weight: 4
+---
+
+# MRR Throughput
+
+Maximum Receive Rate (MRR) tests are complementary to MLRsearch tests,
+as they provide a maximum "raw" throughput benchmark for development and
+testing community. MRR tests measure the packet forwarding rate under
+the maximum load offered by traffic generator over a set trial duration,
+regardless of packet loss.
+
+MRR tests are currently used for following test jobs:
+
+- Report performance comparison: 64B, IMIX for vhost, memif.
+- Daily performance trending: 64B, IMIX for vhost, memif.
+- Per-patch performance verification: 64B.
+- Initial iterations of MLRsearch and PLRsearch: 64B.
+
+Maximum offered load for specific L2 Ethernet frame size is set to
+either the maximum bi-directional link rate or tested NIC model
+capacity, as follows:
+
+- For 10GE NICs the maximum packet rate load is 2x14.88 Mpps for 64B, a
+ 10GE bi-directional link rate.
+- For 25GE NICs the maximum packet rate load is 2x18.75 Mpps for 64B, a
+ 25GE bi-directional link sub-rate limited by 25GE NIC used on TRex TG,
+ XXV710.
+- For 40GE NICs the maximum packet rate load is 2x18.75 Mpps for 64B, a
+ 40GE bi-directional link sub-rate limited by 40GE NIC used on TRex
+ TG,XL710. Packet rate for other tested frame sizes is limited by
+ PCIeGen3 x8 bandwidth limitation of ~50Gbps.
+
+MRR test code implements multiple bursts of offered packet load and has
+two configurable burst parameters: individual trial duration and number
+of trials in a single burst. This enables more precise performance
+trending by providing more results data for analysis.
+
+Burst parameter settings vary between different tests using MRR:
+
+- MRR individual trial duration:
+
+ - Report performance comparison: 1 sec.
+ - Daily performance trending: 1 sec.
+ - Per-patch performance verification: 10 sec.
+ - Initial iteration for MLRsearch: 1 sec.
+ - Initial iteration for PLRsearch: 5.2 sec.
+
+- Number of MRR trials per burst:
+
+ - Report performance comparison: 10.
+ - Daily performance trending: 10.
+ - Per-patch performance verification: 5.
+ - Initial iteration for MLRsearch: 1.
+ - Initial iteration for PLRsearch: 1.
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "PLRsearch"
+weight: 3
+---
+
+# PLRsearch
+
+## Motivation for PLRsearch
+
+Network providers are interested in throughput a system can sustain.
+
+`RFC 2544`[^3] assumes loss ratio is given by a deterministic function of
+offered load. But NFV software systems are not deterministic enough.
+This makes deterministic algorithms (such as `binary search`[^9] per RFC 2544
+and MLRsearch with single trial) to return results,
+which when repeated show relatively high standard deviation,
+thus making it harder to tell what "the throughput" actually is.
+
+We need another algorithm, which takes this indeterminism into account.
+
+## Generic Algorithm
+
+Detailed description of the PLRsearch algorithm is included in the IETF
+draft `draft-vpolak-bmwg-plrsearch-02`[^1] that is in the process
+of being standardized in the IETF Benchmarking Methodology Working Group (BMWG).
+
+### Terms
+
+The rest of this page assumes the reader is familiar with the following terms
+defined in the IETF draft:
+
++ Trial Order Independent System
++ Duration Independent System
++ Target Loss Ratio
++ Critical Load
++ Offered Load regions
+
+ + Zero Loss Region
+ + Non-Deterministic Region
+ + Guaranteed Loss Region
+
++ Fitting Function
+
+ + Stretch Function
+ + Erf Function
+
++ Bayesian Inference
+
+ + Prior distribution
+ + Posterior Distribution
+
++ Numeric Integration
+
+ + Monte Carlo
+ + Importance Sampling
+
+## FD.io CSIT Implementation Specifics
+
+The search receives min_rate and max_rate values, to avoid measurements
+at offered loads not supporeted by the traffic generator.
+
+The implemented tests cases use bidirectional traffic.
+The algorithm stores each rate as bidirectional rate (internally,
+the algorithm is agnostic to flows and directions,
+it only cares about aggregate counts of packets sent and packets lost),
+but debug output from traffic generator lists unidirectional values.
+
+### Measurement Delay
+
+In a sample implemenation in FD.io CSIT project, there is roughly 0.5
+second delay between trials due to restrictons imposed by packet traffic
+generator in use (T-Rex).
+
+As measurements results come in, posterior distribution computation takes
+more time (per sample), although there is a considerable constant part
+(mostly for inverting the fitting functions).
+
+Also, the integrator needs a fair amount of samples to reach the region
+the posterior distribution is concentrated at.
+
+And of course, the speed of the integrator depends on computing power
+of the CPU the algorithm is able to use.
+
+All those timing related effects are addressed by arithmetically increasing
+trial durations with configurable coefficients
+(currently 5.1 seconds for the first trial,
+each subsequent trial being 0.1 second longer).
+
+### Rounding Errors and Underflows
+
+In order to avoid them, the current implementation tracks natural logarithm
+(instead of the original quantity) for any quantity which is never negative.
+Logarithm of zero is minus infinity (not supported by Python),
+so special value "None" is used instead.
+Specific functions for frequent operations (such as "logarithm
+of sum of exponentials") are defined to handle None correctly.
+
+### Fitting Functions
+
+Current implementation uses two fitting functions, called "stretch" and "erf".
+In general, their estimates for critical rate differ,
+which adds a simple source of systematic error,
+on top of randomness error reported by integrator.
+Otherwise the reported stdev of critical rate estimate
+is unrealistically low.
+
+Both functions are not only increasing, but also convex
+(meaning the rate of increase is also increasing).
+
+Both fitting functions have several mathematically equivalent formulas,
+each can lead to an arithmetic overflow or underflow in different sub-terms.
+Overflows can be eliminated by using different exact formulas
+for different argument ranges.
+Underflows can be avoided by using approximate formulas
+in affected argument ranges, such ranges have their own formulas to compute.
+At the end, both fitting function implementations
+contain multiple "if" branches, discontinuities are a possibility
+at range boundaries.
+
+### Prior Distributions
+
+The numeric integrator expects all the parameters to be distributed
+(independently and) uniformly on an interval (-1, 1).
+
+As both "mrr" and "spread" parameters are positive and not dimensionless,
+a transformation is needed. Dimentionality is inherited from max_rate value.
+
+The "mrr" parameter follows a `Lomax distribution`[^4]
+with alpha equal to one, but shifted so that mrr is always greater than 1
+packet per second.
+
+The "stretch" parameter is generated simply as the "mrr" value
+raised to a random power between zero and one;
+thus it follows a `reciprocal distribution`[^5].
+
+### Integrator
+
+After few measurements, the posterior distribution of fitting function
+arguments gets quite concentrated into a small area.
+The integrator is using `Monte Carlo`[^6] with `importance sampling`[^7]
+where the biased distribution is `bivariate Gaussian`[^8] distribution,
+with deliberately larger variance.
+If the generated sample falls outside (-1, 1) interval,
+another sample is generated.
+
+The center and the covariance matrix for the biased distribution
+is based on the first and second moments of samples seen so far
+(within the computation). The center is used directly,
+covariance matrix is scaled up by a heurictic constant (8.0 by default).
+The following additional features are applied
+designed to avoid hyper-focused distributions.
+
+Each computation starts with the biased distribution inherited
+from the previous computation (zero point and unit covariance matrix
+is used in the first computation), but the overal weight of the data
+is set to the weight of the first sample of the computation.
+Also, the center is set to the first sample point.
+When additional samples come, their weight (including the importance correction)
+is compared to sum of the weights of data seen so far (within the iteration).
+If the new sample is more than one e-fold more impactful, both weight values
+(for data so far and for the new sample) are set to (geometric) average
+of the two weights.
+
+This combination showed the best behavior, as the integrator usually follows
+two phases. First phase (where inherited biased distribution
+or single big sample are dominating) is mainly important
+for locating the new area the posterior distribution is concentrated at.
+The second phase (dominated by whole sample population)
+is actually relevant for the critical rate estimation.
+
+### Offered Load Selection
+
+First two measurements are hardcoded to happen at the middle of rate interval
+and at max_rate. Next two measurements follow MRR-like logic,
+offered load is decreased so that it would reach target loss ratio
+if offered load decrease lead to equal decrease of loss rate.
+
+The rest of measurements start directly in between
+erf and stretch estimate average.
+There is one workaround implemented, aimed at reducing the number of consequent
+zero loss measurements (per fitting function). The workaround first stores
+every measurement result which loss ratio was the targed loss ratio or higher.
+Sorted list (called lossy loads) of such results is maintained.
+
+When a sequence of one or more zero loss measurement results is encountered,
+a smallest of lossy loads is drained from the list.
+If the estimate average is smaller than the drained value,
+a weighted average of this estimate and the drained value is used
+as the next offered load. The weight of the estimate decreases exponentially
+with the length of consecutive zero loss results.
+
+This behavior helps the algorithm with convergence speed,
+as it does not need so many zero loss result to get near critical region.
+Using the smallest (not drained yet) of lossy loads makes it sure
+the new offered load is unlikely to result in big loss region.
+Draining even if the estimate is large enough helps to discard
+early measurements when loss hapened at too low offered load.
+Current implementation adds 4 copies of lossy loads and drains 3 of them,
+which leads to fairly stable behavior even for somewhat inconsistent SUTs.
+
+### Caveats
+
+As high loss count measurements add many bits of information,
+they need a large amount of small loss count measurements to balance them,
+making the algorithm converge quite slowly. Typically, this happens
+when few initial measurements suggest spread way bigger then later measurements.
+The workaround in offered load selection helps,
+but more intelligent workarounds could get faster convergence still.
+
+Some systems evidently do not follow the assumption of repeated measurements
+having the same average loss rate (when the offered load is the same).
+The idea of estimating the trend is not implemented at all,
+as the observed trends have varied characteristics.
+
+Probably, using a more realistic fitting functions
+will give better estimates than trend analysis.
+
+## Bottom Line
+
+The notion of Throughput is easy to grasp, but it is harder to measure
+with any accuracy for non-deterministic systems.
+
+Even though the notion of critical rate is harder to grasp than the notion
+of throughput, it is easier to measure using probabilistic methods.
+
+In testing, the difference between througput measurements and critical
+rate measurements is usually small.
+
+In pactice, rules of thumb such as "send at max 95% of purported throughput"
+are common. The correct benchmarking analysis should ask "Which notion is
+95% of throughput an approximation to?" before attempting to answer
+"Is 95% of critical rate safe enough?".
+
+## Algorithmic Analysis
+
+### Motivation
+
+While the estimation computation is based on hard probability science;
+the offered load selection part of PLRsearch logic is pure heuristics,
+motivated by what would a human do based on measurement and computation results.
+
+The quality of any heuristic is not affected by soundness of its motivation,
+just by its ability to achieve the intended goals.
+In case of offered load selection, the goal is to help the search to converge
+to the long duration estimates sooner.
+
+But even those long duration estimates could still be of poor quality.
+Even though the estimate computation is Bayesian (so it is the best it could be
+within the applied assumptions), it can still of poor quality when compared
+to what a human would estimate.
+
+One possible source of poor quality is the randomnes inherently present
+in Monte Carlo numeric integration, but that can be supressed
+by tweaking the time related input parameters.
+
+The most likely source of poor quality then are the assumptions.
+Most importantly, the number and the shape of fitting functions;
+but also others, such as trial order independence and duration independence.
+
+The result can have poor quality in basically two ways.
+One way is related to location. Both upper and lower bounds
+can be overestimates or underestimates, meaning the entire estimated interval
+between lower bound and upper bound lays above or below (respectively)
+of human-estimated interval.
+The other way is related to the estimation interval width.
+The interval can be too wide or too narrow, compared to human estimation.
+
+An estimate from a particular fitting function can be classified
+as an overestimate (or underestimate) just by looking at time evolution
+(without human examining measurement results). Overestimates
+decrease by time, underestimates increase by time (assuming
+the system performance stays constant).
+
+Quality of the width of the estimation interval needs human evaluation,
+and is unrelated to both rate of narrowing (both good and bad estimate intervals
+get narrower at approximately the same relative rate) and relatative width
+(depends heavily on the system being tested).
+
+### Graphical Examples
+
+The following pictures show the upper (red) and lower (blue) bound,
+as well as average of Stretch (pink) and Erf (light green) estimate,
+and offered load chosen (grey), as computed by PLRsearch,
+after each trial measurement within the 30 minute duration of a test run.
+
+Both graphs are focusing on later estimates. Estimates computed from
+few initial measurements are wildly off the y-axis range shown.
+
+The following analysis will rely on frequency of zero loss measurements
+and magnitude of loss ratio if nonzero.
+
+The offered load selection strategy used implies zero loss measurements
+can be gleaned from the graph by looking at offered load points.
+When the points move up farther from lower estimate, it means
+the previous measurement had zero loss. After non-zero loss,
+the offered load starts again right between (the previous values of)
+the estimate curves.
+
+The very big loss ratio results are visible as noticeable jumps
+of both estimates downwards. Medium and small loss ratios are much harder
+to distinguish just by looking at the estimate curves,
+the analysis is based on raw loss ratio measurement results.
+
+The following descriptions should explain why the graphs seem to signal
+low quality estimate at first sight, but a more detailed look
+reveals the quality is good (considering the measurement results).
+
+#### L2 patch
+
+Both fitting functions give similar estimates, the graph shows
+"stochasticity" of measurements (estimates increase and decrease
+within small time regions), and an overall trend of decreasing estimates.
+
+On the first look, the final interval looks fairly narrow,
+especially compared to the region the estimates have travelled
+during the search. But the look at the frequency of zero loss results shows
+this is not a case of overestimation. Measurements at around the same
+offered load have higher probability of zero loss earlier
+(when performed farther from upper bound), but smaller probability later
+(when performed closer to upper bound). That means it is the performance
+of the system under test that decreases (slightly) over time.
+
+With that in mind, the apparent narrowness of the interval
+is not a sign of low quality, just a consequence of PLRsearch assuming
+the performance stays constant.
+
+{{< svg "static/PLR_patch.svg" >}}
+
+#### Vhost
+
+This test case shows what looks like a quite broad estimation interval,
+compared to other test cases with similarly looking zero loss frequencies.
+Notable features are infrequent high-loss measurement results
+causing big drops of estimates, and lack of long-term convergence.
+
+Any convergence in medium-sized intervals (during zero loss results)
+is reverted by the big loss results, as they happen quite far
+from the critical load estimates, and the two fitting functions
+extrapolate differently.
+
+In other words, human only seeing estimates from one fitting function
+would expect narrower end interval, but human seeing the measured loss ratios
+agrees that the interval should be wider than that.
+
+{{< svg "static/PLR_vhost.svg" >}}
+
+#### Summary
+
+The two graphs show the behavior of PLRsearch algorithm applied to soaking test
+when some of PLRsearch assumptions do not hold:
+
++ L2 patch measurement results violate the assumption
+ of performance not changing over time.
++ Vhost measurement results violate the assumption
+ of Poisson distribution matching the loss counts.
+
+The reported upper and lower bounds can have distance larger or smaller
+than a first look by a human would expect, but a more closer look reveals
+the quality is good, considering the circumstances.
+
+The usefullness of the critical load estimate is of questionable value
+when the assumptions are violated.
+
+Some improvements can be made via more specific workarounds,
+for example long term limit of L2 patch performance could be estmated
+by some heuristic.
+
+Other improvements can be achieved only by asking users
+whether loss patterns matter. Is it better to have single digit losses
+distributed fairly evenly over time (as Poisson distribution would suggest),
+or is it better to have short periods of medium losses
+mixed with long periods of zero losses (as happens in Vhost test)
+with the same overall loss ratio?
+
+[^1]: [draft-vpolak-bmwg-plrsearch-02](https://tools.ietf.org/html/draft-vpolak-bmwg-plrsearch-02)
+[^2]: [plrsearch draft](https://tools.ietf.org/html/draft-vpolak-bmwg-plrsearch-00)
+[^3]: [RFC 2544](https://tools.ietf.org/html/rfc2544)
+[^4]: [Lomax distribution](https://en.wikipedia.org/wiki/Lomax_distribution)
+[^5]: [reciprocal distribution](https://en.wikipedia.org/wiki/Reciprocal_distribution)
+[^6]: [Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_integration)
+[^7]: [importance sampling](https://en.wikipedia.org/wiki/Importance_sampling)
+[^8]: [bivariate Gaussian](https://en.wikipedia.org/wiki/Multivariate_normal_distribution)
+[^9]: [binary search](https://en.wikipedia.org/wiki/Binary_search_algorithm)
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "DUT state considerations"
+weight: 6
+---
+
+# DUT state considerations
+
+This page discusses considerations for Device Under Test (DUT) state.
+DUTs such as VPP require configuration, to be provided before the aplication
+starts (via config files) or just after it starts (via API or CLI access).
+
+During operation DUTs gather various telemetry data, depending on configuration.
+This internal state handling is part of normal operation,
+so any performance impact is included in the test results.
+Accessing telemetry data is additional load on DUT,
+so we are not doing that in main trial measurements that affect results,
+but we include separate trials specifically for gathering runtime telemetry.
+
+But there is one kind of state that needs specific handling.
+This kind of DUT state is dynamically created based on incoming traffic,
+it affects how DUT handles the traffic, and (unlike telemetry counters)
+it has uneven impact on CPU load.
+Typical example is NAT, where detecting new sessions takes more CPU than
+forwarding packet on existing (open or recently closed) sessions.
+We call DUT configurations with this kind of state "stateful",
+and configurations without them "stateless".
+(Even though stateless configurations contain state described in previous
+paragraphs, and some configuration items may have "stateful" in their name,
+such as stateful ACLs.)
+
+# Stateful DUT configurations
+
+Typically, the level of CPU impact of traffic depends on DUT state.
+The first packets causing DUT state to change have higher impact,
+subsequent packets matching that state have lower impact.
+
+From performance point of view, this is similar to traffic phases
+for stateful protocols, see
+[NGFW draft](https://tools.ietf.org/html/draft-ietf-bmwg-ngfw-performance-05#section-4.3.4).
+In CSIT we borrow the terminology (even if it does not fit perfectly,
+see discussion below). Ramp-up traffic causes the state change,
+sustain traffic does not change the state.
+
+As the performance is different, each test has to choose which traffic
+it wants to test, and manipulate the DUT state to achieve the intended impact.
+
+## Ramp-up trial
+
+Tests aiming at sustain performance need to make sure DUT state is created.
+We achieve this via a ramp-up trial, specific purpose of which
+is to create the state.
+
+Subsequent trials need no specific handling, as long as the state
+remains the same. But some state can time-out, so additional ramp-up
+trials are inserted whenever the code detects the state can time-out.
+Note that a trial with zero loss refreshes the state,
+so only the time since the last non-zero loss trial is tracked.
+
+For the state to be set completely, it is important both DUT and TG
+do not lose any packets. We achieve this by setting the profile multiplier
+(TPS from now on) to low enough value.
+
+It is also important each state-affecting packet is sent.
+For size-limited traffic profile it is guaranteed by the size limit.
+For continuous traffic, we set a long enough duration (based on TPS).
+
+At the end of the ramp-up trial, we check DUT state to confirm
+it has been created as expected.
+Test fails if the state is not (completely) created.
+
+## State Reset
+
+Tests aiming at ramp-up performance do not use ramp-up trial,
+and they need to reset the DUT state before each trial measurement.
+The way of resetting the state depends on test,
+usually an API call is used to partially de-configure
+the part that holds the state, and then re-configure it back.
+
+In CSIT we control the DUT state behavior via a test variable "resetter".
+If it is not set, DUT state is not reset.
+If it is set, each search algorithm (including MRR) will invoke it
+before all trial measurements (both main and telemetry ones).
+Any configuration keyword enabling a feature with DUT state
+will check whether a test variable for ramp-up rate is present.
+If it is present, resetter is not set.
+If it is not present, the keyword sets the apropriate resetter value.
+This logic makes sure either ramp-up or state reset are used.
+
+Notes: If both ramp-up and state reset were used, the DUT behavior
+would be identical to just reset, while test would take longer to execute.
+If neither were used, DUT will show different performance in subsequent trials,
+violating assumptions of search algorithms.
+
+## DUT versus protocol ramp-up
+
+There are at least three different causes for bandwidth possibly increasing
+within a single measurement trial.
+
+The first is DUT switching from state modification phase to constant phase,
+it is the primary focus of this document.
+Using ramp-up traffic before main trials eliminates this cause
+for tests wishing to measure the performance of the next phase.
+Using size-limited profiles eliminates the next phase
+for tests wishing to measure performance of this phase.
+
+The second is protocol such as TCP ramping up their throughput to utilize
+the bandwidth available. This is the original meaning of "ramp up"
+in the NGFW draft (see above).
+In existing tests we are not using this meaning of TCP ramp-up.
+Instead we use only small transactions, and large enough initial window
+so TCP acts as ramped-up already.
+
+The third is TCP increasing offered load due to retransmissions triggered by
+packet loss. In CSIT we again try to avoid this behavior
+by using small enough data to transfer, so overlap of multiple transactions
+(primary cause of packet loss) is unlikely.
+But in MRR tests, packet loss and non-constant offered load are still expected.
+
+# Stateless DUT configuratons
+
+These are simple configurations, which do not set any resetter value
+(even if ramp-up duration is not configured).
+Majority of existing tests are of this type, using continuous traffic profiles.
+
+In order to identify limits of Trex performance,
+we have added suites with stateless DUT configuration (VPP ip4base)
+subjected to size-limited ASTF traffic.
+The discovered rates serve as a basis of comparison
+for evaluating the results for stateful DUT configurations (VPP NAT44ed)
+subjected to the same traffic profiles.
+
+# DUT versus TG state
+
+Traffic Generator profiles can be stateful (ASTF) or stateless (STL).
+DUT configuration can be stateful or stateless (with respect to packet traffic).
+
+In CSIT we currently use all four possible configurations:
+
+- Regular stateless VPP tests use stateless traffic profiles.
+
+- Stateless VPP configuration with stateful profile is used as a base for
+ comparison.
+
+- Some stateful DUT configurations (NAT44DET, NAT44ED unidirectional)
+ are tested using stateless traffic profiles and continuous traffic.
+
+- The rest of stateful DUT configurations (NAT44ED bidirectional)
+ are tested using stateful traffic profiles and size limited traffic.
--- /dev/null
+---
+bookToc: false
+title: "Generic Segmentation Offload"
+weight: 15
+---
+
+# Generic Segmentation Offload
+
+## Overview
+
+Generic Segmentation Offload (GSO) reduces per-packet processing
+overhead by enabling applications to pass a multi-packet buffer to
+(v)NIC and process a smaller number of large packets (e.g. frame size of
+64 KB), instead of processing higher numbers of small packets (e.g.
+frame size of 1500 B), thus reducing per-packet overhead.
+
+GSO tests for VPP vhostuser and tapv2 interfaces. All tests cases use iPerf3
+client and server applications running TCP/IP as a traffic generator. For
+performance comparison the same tests are run without GSO enabled.
+
+## GSO Test Topologies
+
+Two VPP GSO test topologies are implemented:
+
+1. iPerfC_GSOvirtio_LinuxVM --- GSOvhost_VPP_GSOvhost --- iPerfS_GSOvirtio_LinuxVM
+
+ - Tests VPP GSO on vhostuser interfaces and interaction with Linux
+ virtio with GSO enabled.
+
+2. iPerfC_GSOtap_LinuxNspace --- GSOtapv2_VPP_GSOtapv2 --- iPerfS_GSOtap_LinuxNspace
+
+ - Tests VPP GSO on tapv2 interfaces and interaction with Linux tap
+ with GSO enabled.
+
+Common configuration:
+
+- iPerfC (client) and iPerfS (server) run in TCP/IP mode without upper
+ bandwidth limit.
+- Trial duration is set to 30 sec.
+- iPerfC, iPerfS and VPP run in the single SUT node.
+
+
+## VPP GSOtap Topology
+
+### VPP Configuration
+
+VPP GSOtap tests are executed without using hyperthreading. VPP worker runs on
+a single core. Multi-core tests are not executed. Each interface belongs to
+separate namespace. Following core pinning scheme is used:
+
+- 1t1c (rxq=1, rx_qsz=4096, tx_qsz=4096)
+ - system isolated: 0,28,56,84
+ - vpp mt: 1
+ - vpp wt: 2
+ - vhost: 3-5
+ - iperf-s: 6
+ - iperf-c: 7
+
+### iPerf3 Server Configuration
+
+iPerf3 version used 3.7
+
+ $ sudo -E -S ip netns exec tap1_namespace iperf3 \
+ --server --daemon --pidfile /tmp/iperf3_server.pid --logfile /tmp/iperf3.log --port 5201 --affinity <X>
+
+For the full iPerf3 reference please see:
+[iPerf3 docs](https://github.com/esnet/iperf/blob/master/docs/invoking.rst).
+
+
+### iPerf3 Client Configuration
+
+iPerf3 version used 3.7
+
+ $ sudo -E -S ip netns exec tap1_namespace iperf3 \
+ --client 2.2.2.2 --bind 1.1.1.1 --port 5201 --parallel <Y> --time 30.0 --affinity <X> --zerocopy
+
+For the full iPerf3 reference please see:
+[iPerf3 docs](https://github.com/esnet/iperf/blob/master/docs/invoking.rst).
+
+
+## VPP GSOvhost Topology
+
+### VPP Configuration
+
+VPP GSOvhost tests are executed without using hyperthreading. VPP worker runs
+on a single core. Multi-core tests are not executed. Following core pinning
+scheme is used:
+
+- 1t1c (rxq=1, rx_qsz=1024, tx_qsz=1024)
+ - system isolated: 0,28,56,84
+ - vpp mt: 1
+ - vpp wt: 2
+ - vm-iperf-s: 3,4,5,6,7
+ - vm-iperf-c: 8,9,10,11,12
+ - iperf-s: 1
+ - iperf-c: 1
+
+### iPerf3 Server Configuration
+
+iPerf3 version used 3.7
+
+ $ sudo iperf3 \
+ --server --daemon --pidfile /tmp/iperf3_server.pid --logfile /tmp/iperf3.log --port 5201 --affinity X
+
+For the full iPerf3 reference please see:
+[iPerf3 docs](https://github.com/esnet/iperf/blob/master/docs/invoking.rst).
+
+
+### iPerf3 Client Configuration
+
+iPerf3 version used 3.7
+
+ $ sudo iperf3 \
+ --client 2.2.2.2 --bind 1.1.1.1 --port 5201 --parallel <Y> --time 30.0 --affinity X --zerocopy
+
+For the full iPerf3 reference please see:
+[iPerf3 docs](https://github.com/esnet/iperf/blob/master/docs/invoking.rst).
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "GENEVE"
+weight: 21
+---
+
+# GENEVE
+
+## GENEVE Prefix Bindings
+
+GENEVE prefix bindings should be representative to target applications, where
+a packet flows of particular set of IPv4 addresses (L3 underlay network) is
+routed via dedicated GENEVE interface by building an L2 overlay.
+
+Private address ranges to be used in tests:
+
+- East hosts ip address range: 10.0.1.0 - 10.127.255.255 (10.0/9 prefix)
+
+ - Total of 2^23 - 256 (8 388 352) of usable IPv4 addresses
+ - Usable in tests for up to 32 767 GENEVE tunnels (IPv4 underlay networks)
+
+- West hosts ip address range: 10.128.1.0 - 10.255.255.255 (10.128/9 prefix)
+
+ - Total of 2^23 - 256 (8 388 352) of usable IPv4 addresses
+ - Usable in tests for up to 32 767 GENEVE tunnels (IPv4 underlay networks)
+
+## GENEVE Tunnel Scale
+
+If N is a number of GENEVE tunnels (and IPv4 underlay networks) then TG sends
+256 packet flows in every of N different sets:
+
+- i = 1,2,3, ... N - GENEVE tunnel index
+
+- East-West direction: GENEVE encapsulated packets
+
+ - Outer IP header:
+
+ - src ip: 1.1.1.1
+
+ - dst ip: 1.1.1.2
+
+ - GENEVE header:
+
+ - vni: i
+
+ - Inner IP header:
+
+ - src_ip_range(i) = 10.(0 + rounddown(i/255)).(modulo(i/255)).(0-to-255)
+
+ - dst_ip_range(i) = 10.(128 + rounddown(i/255)).(modulo(i/255)).(0-to-255)
+
+- West-East direction: non-encapsulated packets
+
+ - IP header:
+
+ - src_ip_range(i) = 10.(128 + rounddown(i/255)).(modulo(i/255)).(0-to-255)
+
+ - dst_ip_range(i) = 10.(0 + rounddown(i/255)).(modulo(i/255)).(0-to-255)
+
+ **geneve-tunnels** | **total-flows**
+-------------------:|----------------:
+ 1 | 256
+ 4 | 1 024
+ 16 | 4 096
+ 64 | 16 384
+ 256 | 65 536
+ 1 024 | 262 144
\ No newline at end of file
--- /dev/null
+---
+bookCollapseSection: true
+bookFlatSection: false
+title: "Hoststack Testing"
+weight: 14
+---
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "QUIC/UDP/IP with vpp_echo"
+weight: 1
+---
+
+# QUIC/UDP/IP with vpp_echo
+
+[vpp_echo performance testing tool](https://wiki.fd.io/view/VPP/HostStack#External_Echo_Server.2FClient_.28vpp_echo.29)
+is a bespoke performance test application which utilizes the 'native
+HostStack APIs' to verify performance and correct handling of
+connection/stream events with uni-directional and bi-directional
+streams of data.
+
+Because iperf3 does not support the QUIC transport protocol, vpp_echo
+is used for measuring the maximum attainable goodput of the VPP Host
+Stack connection utilizing the QUIC transport protocol across two
+instances of VPP running on separate DUT nodes. The QUIC transport
+protocol supports multiple streams per connection and test cases
+utilize different combinations of QUIC connections and number of
+streams per connection.
+
+The test configuration is as follows:
+
+ DUT1 Network DUT2
+ [ vpp_echo-client -> VPP1 ]=======[ VPP2 -> vpp_echo-server]
+ N-streams/connection
+
+where,
+
+1. vpp_echo server attaches to VPP2 and LISTENs on VPP2:TCP port 1234.
+2. vpp_echo client creates one or more connections to VPP1 and opens
+ one or more stream per connection to VPP2:TCP port 1234.
+3. vpp_echo client transmits a uni-directional stream as fast as the
+ VPP Host Stack allows to the vpp_echo server for the test duration.
+4. At the end of the test the vpp_echo client emits the goodput
+ measurements for all streams and the sum of all streams.
+
+Test cases include
+
+1. 1 QUIC Connection with 1 Stream
+2. 1 QUIC connection with 10 Streams
+3. 10 QUIC connetions with 1 Stream
+4. 10 QUIC connections with 10 Streams
+
+with stream sizes to provide reasonable test durations. The VPP Host
+Stack QUIC transport is configured to utilize the picotls encryption
+library. In the future, tests utilizing addtional encryption
+algorithms will be added.
--- /dev/null
+---
+bookToc: false
+title: "TCP/IP with iperf3"
+weight: 2
+---
+
+# TCP/IP with iperf3
+
+[iperf3 goodput measurement tool](https://github.com/esnet/iperf)
+is used for measuring the maximum attainable goodput of the VPP Host
+Stack connection across two instances of VPP running on separate DUT
+nodes. iperf3 is a popular open source tool for active measurements
+of the maximum achievable goodput on IP networks.
+
+Because iperf3 utilizes the POSIX socket interface APIs, the current
+test configuration utilizes the LD_PRELOAD mechanism in the linux
+kernel to connect iperf3 to the VPP Host Stack using the VPP
+Communications Library (VCL) LD_PRELOAD library (libvcl_ldpreload.so).
+
+In the future, a forked version of iperf3 which has been modified to
+directly use the VCL application APIs may be added to determine the
+difference in performance of 'VCL Native' applications versus utilizing
+LD_PRELOAD which inherently has more overhead and other limitations.
+
+The test configuration is as follows:
+
+ DUT1 Network DUT2
+ [ iperf3-client -> VPP1 ]=======[ VPP2 -> iperf3-server]
+
+where,
+
+1. iperf3 server attaches to VPP2 and LISTENs on VPP2:TCP port 5201.
+2. iperf3 client attaches to VPP1 and opens one or more stream
+ connections to VPP2:TCP port 5201.
+3. iperf3 client transmits a uni-directional stream as fast as the
+ VPP Host Stack allows to the iperf3 server for the test duration.
+4. At the end of the test the iperf3 client emits the goodput
+ measurements for all streams and the sum of all streams.
+
+Test cases include 1 and 10 Streams with a 20 second test duration
+with the VPP Host Stack configured to utilize the Cubic TCP
+congestion algorithm.
+
+Note: iperf3 is single threaded, so it is expected that the 10 stream
+test shows little or no performance improvement due to
+multi-thread/multi-core execution.
+
+There are also variations of these test cases which use the VPP Network
+Simulator (NSIM) plugin to test the VPP Hoststack goodput with 1 percent
+of the traffic being dropped at the output interface of VPP1 thereby
+simulating a lossy network. The NSIM tests are experimental and the
+test results are not currently representative of typical results in a
+lossy network.
--- /dev/null
+---
+bookToc: false
+title: "UDP/IP with iperf3"
+weight: 3
+---
+
+# UDP/IP with iperf3
+
+[iperf3 goodput measurement tool](https://github.com/esnet/iperf)
+is used for measuring the maximum attainable goodput of the VPP Host
+Stack connection across two instances of VPP running on separate DUT
+nodes. iperf3 is a popular open source tool for active measurements
+of the maximum achievable goodput on IP networks.
+
+Because iperf3 utilizes the POSIX socket interface APIs, the current
+test configuration utilizes the LD_PRELOAD mechanism in the linux
+kernel to connect iperf3 to the VPP Host Stack using the VPP
+Communications Library (VCL) LD_PRELOAD library (libvcl_ldpreload.so).
+
+In the future, a forked version of iperf3 which has been modified to
+directly use the VCL application APIs may be added to determine the
+difference in performance of 'VCL Native' applications versus utilizing
+LD_PRELOAD which inherently has more overhead and other limitations.
+
+The test configuration is as follows:
+
+ DUT1 Network DUT2
+ [ iperf3-client -> VPP1 ]=======[ VPP2 -> iperf3-server]
+
+where,
+
+1. iperf3 server attaches to VPP2 and LISTENs on VPP2:UDP port 5201.
+2. iperf3 client attaches to VPP1 and transmits one or more streams
+ of packets to VPP2:UDP port 5201.
+3. iperf3 client transmits a uni-directional stream as fast as the
+ VPP Host Stack allows to the iperf3 server for the test duration.
+4. At the end of the test the iperf3 client emits the goodput
+ measurements for all streams and the sum of all streams.
+
+Test cases include 1 and 10 Streams with a 20 second test duration
+with the VPP Host Stack using the UDP transport layer..
+
+Note: iperf3 is single threaded, so it is expected that the 10 stream
+test shows little or no performance improvement due to
+multi-thread/multi-core execution.
--- /dev/null
+---
+bookToc: false
+title: "VSAP ab with nginx"
+weight: 4
+---
+
+# VSAP ab with nginx
+
+[VSAP (VPP Stack Acceleration Project)](https://wiki.fd.io/view/VSAP)
+aims to establish an industry user space application ecosystem based on
+the VPP hoststack. As a pre-requisite to adapting open source applications
+using VPP Communications Library to accelerate performance, the VSAP team
+has introduced baseline tests utilizing the LD_PRELOAD mechanism to capture
+baseline performance data.
+
+[AB (Apache HTTP server benchmarking tool)](https://httpd.apache.org/docs/2.4/programs/ab.html)
+is used for measuring the maximum connections-per-second and requests-per-second.
+
+[NGINX](https://www.nginx.com) is a popular open source HTTP server
+application. Because NGINX utilizes the POSIX socket interface APIs, the test
+configuration uses the LD_PRELOAD mechanism to connect NGINX to the VPP
+Hoststack using the VPP Communications Library (VCL) LD_PRELOAD library
+(libvcl_ldpreload.so).
+
+In the future, a version of NGINX which has been modified to
+directly use the VCL application APIs will be added to determine the
+difference in performance of 'VCL Native' applications versus utilizing
+LD_PRELOAD which inherently has more overhead and other limitations.
+
+The test configuration is as follows:
+
+ TG Network DUT
+ [ AB ]=============[ VPP -> nginx ]
+
+where,
+
+1. nginx attaches to VPP and listens on TCP port 80
+2. ab runs CPS and RPS tests with packets flowing from the Test Generator node,
+ across 100G NICs, through VPP hoststack to NGINX.
+3. At the end of the tests, the results are reported by AB.
--- /dev/null
+---
+bookToc: false
+title: "Internet Protocol Security (IPsec)"
+weight: 11
+---
+
+# Internet Protocol Security (IPsec)
+
+VPP IPsec performance tests are executed for the following crypto
+plugins:
+
+- `crypto_native`, used for software based crypto leveraging CPU
+ platform optimizations e.g. Intel's AES-NI instruction set.
+- `crypto_ipsecmb`, used for hardware based crypto with Intel QAT PCIe
+ cards.
+
+## IPsec with VPP Native SW Crypto
+
+CSIT implements following IPsec test cases relying on VPP native crypto
+(`crypto_native` plugin):
+
+ **VPP Crypto Engine** | **ESP Encryption** | **ESP Integrity** | **Scale Tested**
+----------------------:|-------------------:|------------------:|-----------------:
+ crypto_native | AES[128\|256]-GCM | GCM | 1 to 60k tunnels
+ crypto_native | AES128-CBC | SHA[256\|512] | 1 to 60k tunnels
+
+VPP IPsec with SW crypto are executed in both tunnel and policy modes,
+with tests running on 3-node testbeds: 3n-icx, 3n-tsh.
+
+## IPsec with Intel QAT HW
+
+CSIT implements following IPsec test cases relying on ipsecmb library
+(`crypto_ipsecmb` plugin) and Intel QAT 8950 (50G HW crypto card):
+
+dpdk_cryptodev
+
+ **VPP Crypto Engine** | **VPP Crypto Workers** | **ESP Encryption** | **ESP Integrity** | **Scale Tested**
+----------------------:|-----------------------:|-------------------:|------------------:|-----------------:
+ crypto_ipsecmb | sync/all workers | AES[128\|256]-GCM | GCM | 1, 1k tunnels
+ crypto_ipsecmb | sync/all workers | AES[128]-CBC | SHA[256\|512] | 1, 1k tunnels
+ crypto_ipsecmb | async/crypto worker | AES[128\|256]-GCM | GCM | 1, 4, 1k tunnels
+ crypto_ipsecmb | async/crypto worker | AES[128]-CBC | SHA[256\|512] | 1, 4, 1k tunnels
+
+## IPsec with Async Crypto Feature Workers
+
+*TODO Description to be added*
+
+## IPsec Uni-Directional Tests with VPP Native SW Crypto
+
+CSIT implements following IPsec uni-directional test cases relying on VPP native
+crypto (`crypto_native` plugin) in tunnel mode:
+
+ **VPP Crypto Engine** | **ESP Encryption** | **ESP Integrity** | **Scale Tested**
+----------------------:|-------------------:|------------------:|-------------------:
+ crypto_native | AES[128\|256]-GCM | GCM | 4, 1k, 10k tunnels
+ crypto_native | AES128-CBC | SHA[512] | 4, 1k, 10k tunnels
+
+In policy mode:
+
+ **VPP Crypto Engine** | **ESP Encryption** | **ESP Integrity** | **Scale Tested**
+----------------------:|-------------------:|------------------:|------------------:
+ crypto_native | AES[256]-GCM | GCM | 1, 40, 1k tunnels
+
+The tests are running on 2-node testbeds: 2n-tx2. The uni-directional tests
+are partially addressing a weakness in 2-node testbed setups with T-Rex as
+the traffic generator. With just one DUT node, we can either encrypt or decrypt
+traffic in each direction.
+
+The testcases are only doing encryption - packets are encrypted on the DUT and
+then arrive at TG where no additional packet processing is needed (just
+counting packets).
+
+Decryption would require that the traffic generator generated encrypted packets
+which the DUT then would decrypt. However, T-Rex does not have the capability
+to encrypt packets.
--- /dev/null
+---
+bookToc: false
+title: "Multi-Core Speedup"
+weight: 13
+---
+
+# Multi-Core Speedup
+
+All performance tests are executed with single physical core and with
+multiple cores scenarios.
+
+## Intel Hyper-Threading (HT)
+
+Intel Xeon processors used in FD.io CSIT can operate either in HT
+Disabled mode (single logical core per each physical core) or in HT
+Enabled mode (two logical cores per each physical core). HT setting is
+applied in BIOS and requires server SUT reload for it to take effect,
+making it impractical for continuous changes of HT mode of operation.
+
+Performance tests are executed with server SUTs' Intel XEON processors
+configured with Intel Hyper-Threading Enabled for all Xeon
+Cascadelake and Xeon Icelake testbeds.
+
+## Multi-core Tests
+
+Multi-core tests are executed in the following VPP worker thread and physical
+core configurations:
+
+#. Intel Xeon Icelake and Cascadelake testbeds (2n-icx, 3n-icx, 2n-clx)
+ with Intel HT enabled (2 logical CPU cores per each physical core):
+
+ #. 2t1c - 2 VPP worker threads on 1 physical core.
+ #. 4t2c - 4 VPP worker threads on 2 physical cores.
+ #. 8t4c - 8 VPP worker threads on 4 physical cores.
+
+VPP worker threads are the data plane threads running on isolated
+logical cores. With Intel HT enabled VPP workers are placed as sibling
+threads on each used physical core. VPP control threads (main, stats)
+are running on a separate non-isolated core together with other Linux
+processes.
+
+In all CSIT tests care is taken to ensure that each VPP worker handles
+the same amount of received packet load and does the same amount of
+packet processing work. This is achieved by evenly distributing per
+interface type (e.g. physical, virtual) receive queues over VPP workers
+using default VPP round-robin mapping and by loading these queues with
+the same amount of packet flows.
+
+If number of VPP workers is higher than number of physical or virtual
+interfaces, multiple receive queues are configured on each interface.
+NIC Receive Side Scaling (RSS) for physical interfaces and multi-queue
+for virtual interfaces are used for this purpose.
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "Network Address Translation"
+weight: 7
+---
+
+# Network Address Translation
+
+## NAT44 Prefix Bindings
+
+NAT44 prefix bindings should be representative to target applications,
+where a number of private IPv4 addresses from the range defined by
+RFC1918 is mapped to a smaller set of public IPv4 addresses from the
+public range.
+
+Following quantities are used to describe inside to outside IP address
+and port bindings scenarios:
+
+- Inside-addresses, number of inside source addresses
+ (representing inside hosts).
+- Ports-per-inside-address, number of TCP/UDP source
+ ports per inside source address.
+- Outside-addresses, number of outside (public) source addresses
+ allocated to NAT44.
+- Ports-per-outside-address, number of TCP/UDP source
+ ports per outside source address. The maximal number of
+ ports-per-outside-address usable for NAT is 64 512
+ (in non-reserved port range 1024-65535, RFC4787).
+- Sharing-ratio, equal to inside-addresses divided by outside-addresses.
+
+CSIT NAT44 tests are designed to take into account the maximum number of
+ports (sessions) required per inside host (inside-address) and at the
+same time to maximize the use of outside-address range by using all
+available outside ports. With this in mind, the following scheme of
+NAT44 sharing ratios has been devised for use in CSIT:
+
+ **ports-per-inside-address** | **sharing-ratio**
+-----------------------------:|------------------:
+ 63 | 1024
+ 126 | 512
+ 252 | 256
+ 504 | 128
+
+Initial CSIT NAT44 tests, including associated TG/TRex traffic profiles,
+are based on ports-per-inside-address set to 63 and the sharing ratio of
+1024. This approach is currently used for all NAT44 tests including
+NAT44det (NAT44 deterministic used for Carrier Grade NAT applications)
+and NAT44ed (Endpoint Dependent).
+
+Private address ranges to be used in tests:
+
+- 192.168.0.0 - 192.168.255.255 (192.168/16 prefix)
+
+ - Total of 2^16 (65 536) of usable IPv4 addresses.
+ - Used in tests for up to 65 536 inside addresses (inside hosts).
+
+- 172.16.0.0 - 172.31.255.255 (172.16/12 prefix)
+
+ - Total of 2^20 (1 048 576) of usable IPv4 addresses.
+ - Used in tests for up to 1 048 576 inside addresses (inside hosts).
+
+### NAT44 Session Scale
+
+NAT44 session scale tested is govern by the following logic:
+
+- Number of inside-addresses(hosts) H[i] = (H[i-1] x 2^2) with H(0)=1 024,
+ i = 1,2,3, ...
+
+ - H[i] = 1 024, 4 096, 16 384, 65 536, 262 144, ...
+
+- Number of sessions S[i] = H[i] * ports-per-inside-address
+
+ - ports-per-inside-address = 63
+
+ **i** | **hosts** | **sessions**
+------:|----------:|-------------:
+ 0 | 1 024 | 64 512
+ 1 | 4 096 | 258 048
+ 2 | 16 384 | 1 032 192
+ 3 | 65 536 | 4 128 768
+ 4 | 262 144 | 16 515 072
+
+### NAT44 Deterministic
+
+NAT44det performance tests are using TRex STL (Stateless) API and traffic
+profiles, similar to all other stateless packet forwarding tests like
+ip4, ip6 and l2, sending UDP packets in both directions
+inside-to-outside and outside-to-inside.
+
+The inside-to-outside traffic uses single destination address (20.0.0.0)
+and port (1024).
+The inside-to-outside traffic covers whole inside address and port range,
+the outside-to-inside traffic covers whole outside address and port range.
+
+NAT44det translation entries are created during the ramp-up phase,
+followed by verification that all entries are present,
+before proceeding to the main measurements of the test.
+This ensures session setup does not impact the forwarding performance test.
+
+Associated CSIT test cases use the following naming scheme to indicate
+NAT44det scenario tested:
+
+- ethip4udp-nat44det-h{H}-p{P}-s{S}-[mrr|ndrpdr|soak]
+
+ - {H}, number of inside hosts, H = 1024, 4096, 16384, 65536, 262144.
+ - {P}, number of ports per inside host, P = 63.
+ - {S}, number of sessions, S = 64512, 258048, 1032192, 4128768,
+ 16515072.
+ - [mrr|ndrpdr|soak], MRR, NDRPDR or SOAK test.
+
+### NAT44 Endpoint-Dependent
+
+In order to excercise NAT44ed ability to translate based on both
+source and destination address and port, the inside-to-outside traffic
+varies also destination address and port. Destination port is the same
+as source port, destination address has the same offset as the source address,
+but applied to different subnet (starting with 20.0.0.0).
+
+As the mapping is not deterministic (for security reasons),
+we cannot easily use stateless bidirectional traffic profiles.
+Inside address and port range is fully covered,
+but we do not know which outside-to-inside source address and port to use
+to hit an open session.
+
+Therefore, NAT44ed is benchmarked using following methodologies:
+
+- Unidirectional throughput using *stateless* traffic profile.
+- Connections-per-second (CPS) using *stateful* traffic profile.
+- Bidirectional throughput (TPUT, see below) using *stateful* traffic profile.
+
+Unidirectional NAT44ed throughput tests are using TRex STL (Stateless)
+APIs and traffic profiles, but with packets sent only in
+inside-to-outside direction.
+Similarly to NAT44det, NAT44ed unidirectional throughput tests include
+a ramp-up phase to establish and verify the presence of required NAT44ed
+binding entries. As the sessions have finite duration, the test code
+keeps inserting ramp-up trials during the search, if it detects a risk
+of sessions timing out. Any zero loss trial visits all sessions,
+so it acts also as a ramp-up.
+
+Stateful NAT44ed tests are using TRex ASTF (Advanced Stateful) APIs and
+traffic profiles, with packets sent in both directions. Tests are run
+with both UDP and TCP sessions.
+As NAT44ed CPS (connections-per-second) stateful tests
+measure (also) session opening performance,
+they use state reset instead of ramp-up trial.
+NAT44ed TPUT (bidirectional throughput) tests prepend ramp-up trials
+as in the unidirectional tests,
+so the test results describe performance without translation entry
+creation overhead.
+
+Associated CSIT test cases use the following naming scheme to indicate
+NAT44det case tested:
+
+- Stateless: ethip4udp-nat44ed-h{H}-p{P}-s{S}-udir-[mrr|ndrpdr|soak]
+
+ - {H}, number of inside hosts, H = 1024, 4096, 16384, 65536, 262144.
+ - {P}, number of ports per inside host, P = 63.
+ - {S}, number of sessions, S = 64512, 258048, 1032192, 4128768,
+ 16515072.
+ - udir-[mrr|ndrpdr|soak], unidirectional stateless tests MRR, NDRPDR
+ or SOAK.
+
+- Stateful: ethip4[udp|tcp]-nat44ed-h{H}-p{P}-s{S}-[cps|tput]-[mrr|ndrpdr|soak]
+
+ - [udp|tcp], UDP or TCP sessions
+ - {H}, number of inside hosts, H = 1024, 4096, 16384, 65536, 262144.
+ - {P}, number of ports per inside host, P = 63.
+ - {S}, number of sessions, S = 64512, 258048, 1032192, 4128768,
+ 16515072.
+ - [cps|tput], connections-per-second session establishment rate or
+ packets-per-second average rate, or packets-per-second rate
+ without session establishment.
+ - [mrr|ndrpdr|soak], bidirectional stateful tests MRR, NDRPDR, or SOAK.
+
+## Stateful traffic profiles
+
+There are several important details which distinguish ASTF profiles
+from stateless profiles.
+
+### General considerations
+
+#### Protocols
+
+ASTF profiles are limited to either UDP or TCP protocol.
+
+#### Programs
+
+Each template in the profile defines two "programs", one for the client side
+and one for the server side.
+
+Each program specifies when that side has to wait until enough data is received
+(counted in packets for UDP and in bytes for TCP)
+and when to send additional data. Together, the two programs
+define a single transaction. Due to packet loss, transaction may take longer,
+use more packets (retransmission) or never finish in its entirety.
+
+#### Instances
+
+A client instance is created according to TPS parameter for the trial,
+and sends the first packet of the transaction (in some cases more packets).
+Each client instance uses a different source address (see sequencing below)
+and some source port. The destination address also comes from a range,
+but destination port has to be constant for a given program.
+
+TRex uses an opaque way to chose source ports, but as session counting shows,
+next client with the same source address uses a different source port.
+
+Server instance is created when the first packet arrives to the server side.
+Source address and port of the first packet are used as destination address
+and port for the server responses. This is the ability we need
+when outside surface is not predictable.
+
+When a program reaches its end, the instance is deleted.
+This creates possible issues with server instances. If the server instance
+does not read all the data client has sent, late data packets
+can cause a second copy of server instance to be created,
+which breaks assumptions on how many packet a transaction should have.
+
+The need for server instances to read all the data reduces the overall
+bandwidth TRex is able to create in ASTF mode.
+
+Note that client instances are not created on packets,
+so it is safe to end client program without reading all server data
+(unless the definition of transaction success requires that).
+
+#### Sequencing
+
+ASTF profiles offer two modes for choosing source and destination IP addresses
+for client programs: seqential and pseudorandom.
+In current tests we are using sequential addressing only (if destination
+address varies at all).
+
+For client destination UDP/TCP port, we use a single constant value.
+(TRex can support multiple program pairs in the same traffic profile,
+distinguished by the port number.)
+
+#### Transaction overlap
+
+If a transaction takes longer to finish, compared to period implied by TPS,
+TRex will have multiple client or server instances active at a time.
+
+During calibration testing we have found this increases CPU utilization,
+and for high TPS it can lead to TRex's Rx or Tx buffers becoming full.
+This generally leads to duration stretching, and/or packet loss on TRex.
+
+Currently used transactions were chosen to be short, so risk of bad behavior
+is decreased. But in MRR tests, where load is computed based on NIC ability,
+not TRex ability, anomalous behavior is still possible
+(e.g. MRR values being way lower than NDR).
+
+#### Delays
+
+TRex supports adding constant delays to ASTF programs.
+This can be useful, for example if we want to separate connection establishment
+from data transfer.
+
+But as TRex tracks delayed instances as active, this still results
+in higher CPU utilization and reduced performance issues
+(as other overlaping transactions). So the current tests do not use any delays.
+
+#### Keepalives
+
+Both UDP and TCP protocol implementations in TRex programs support keepalive
+duration. That means there is a configurable period of keepalive time,
+and TRex sends keepalive packets automatically (outside the program)
+for the time the program is active (started, not ended yet)
+but not sending any packets.
+
+For TCP this is generally not a big deal, as the other side usually
+retransmits faster. But for UDP it means a packet loss may leave
+the receiving program running.
+
+In order to avoid keepalive packets, keepalive value is set to a high number.
+Here, "high number" means that even at maximum scale and minimum TPS,
+there are still no keepalive packets sent within the corresponding
+(computed) trial duration. This number is kept the same also for
+smaller scale traffic profiles, to simplify maintenance.
+
+#### Transaction success
+
+The transaction is considered successful at Layer-7 (L7) level
+when both program instances close. At this point, various L7 counters
+(unofficial name) are updated on TRex.
+
+We found that proper close and L7 counter update can be CPU intensive,
+whereas lower-level counters (ipackets, opackets) called L2 counters
+can keep up with higher loads.
+
+For some tests, we do not need to confirm the whole transaction was successful.
+CPS (connections per second) tests are a typical example.
+We care only for NAT44ed creating a session (needs one packet
+in inside-to-outside direction per session) and being able to use it
+(needs one packet in outside-to-inside direction).
+
+Similarly in TPUT tests (packet throuput, counting both control
+and data packets), we care about NAT44ed ability to forward packets,
+we do not care whether aplications (TRex) can fully process them at that rate.
+
+Therefore each type of tests has its own formula (usually just one counter
+already provided by TRex) to count "successful enough" transactions
+and attempted transactions. Currently, all tests relying on L7 counters
+use size-limited profiles, so they know what the count of attempted
+transactions should be, but due to duration stretching
+TRex might have been unable to send that many packets.
+For search purposes, unattempted transactions are treated the same
+as attempted but failed transactions.
+
+Sometimes even the number of transactions as tracked by search algorithm
+does not match the transactions as defined by ASTF programs.
+See TCP TPUT profile below.
+
+### UDP CPS
+
+This profile uses a minimalistic transaction to verify NAT44ed session has been
+created and it allows outside-to-inside traffic.
+
+Client instance sends one packet and ends.
+Server instance sends one packet upon creation and ends.
+
+In principle, packet size is configurable,
+but currently used tests apply only one value (100 bytes frame).
+
+Transaction counts as attempted when opackets counter increases on client side.
+Transaction counts as successful when ipackets counter increases on client side.
+
+### TCP CPS
+
+This profile uses a minimalistic transaction to verify NAT44ed session has been
+created and it allows outside-to-inside traffic.
+
+Client initiates TCP connection. Client waits until connection is confirmed
+(by reading zero data bytes). Client ends.
+Server accepts the connection. Server waits for indirect confirmation
+from client (by waiting for client to initiate close). Server ends.
+
+Without packet loss, the whole transaction takes 7 packets to finish
+(4 and 3 per direction).
+From NAT44ed point of view, only the first two are needed to verify
+the session got created.
+
+Packet size is not configurable, but currently used tests report
+frame size as 64 bytes.
+
+Transaction counts as attempted when tcps_connattempt counter increases
+on client side.
+Transaction counts as successful when tcps_connects counter increases
+on client side.
+
+### UDP TPUT
+
+This profile uses a small transaction of "request-response" type,
+with several packets simulating data payload.
+
+Client sends 5 packets and closes immediately.
+Server reads all 5 packets (needed to avoid late packets creating new
+server instances), then sends 5 packets and closes.
+The value 5 was chosen to mirror what TCP TPUT (see below) choses.
+
+Packet size is configurable, currently we have tests for 100,
+1518 and 9000 bytes frame (to match size of TCP TPUT data frames, see below).
+
+As this is a packet oriented test, we do not track the whole
+10 packet transaction. Similarly to stateless tests, we treat each packet
+as a "transaction" for search algorthm packet loss ratio purposes.
+Therefore a "transaction" is attempted when opacket counter on client
+or server side is increased. Transaction is successful if ipacket counter
+on client or server side is increased.
+
+If one of 5 client packets is lost, server instance will get stuck
+in the reading phase. This probably decreases TRex performance,
+but it leads to more stable results then alternatives.
+
+### TCP TPUT
+
+This profile uses a small transaction of "request-response" type,
+with some data amount to be transferred both ways.
+
+In CSIT release 22.06, TRex behavior changed, so we needed to edit
+the traffic profile. Let us describe the pre-22.06 profile first.
+
+Client connects, sends 5 data packets worth of data,
+receives 5 data packets worth of data and closes its side of the connection.
+Server accepts connection, reads 5 data packets worth of data,
+sends 5 data packets worth of data and closes its side of the connection.
+As usual in TCP, sending side waits for ACK from the receiving side
+before proceeding with next step of its program.
+
+Server read is needed to avoid premature close and second server instance.
+Client read is not stricly needed, but ACKs allow TRex to close
+the server instance quickly, thus saving CPU and improving performance.
+
+The number 5 of data packets was chosen so TRex is able to send them
+in a single burst, even with 9000 byte frame size (TRex has a hard limit
+on initial window size).
+That leads to 16 packets (9 of them in c2s direction) to be exchanged
+if no loss occurs.
+The size of data packets is controlled by the traffic profile setting
+the appropriate maximum segment size. Due to TRex restrictions,
+the minimal size for IPv4 data frame achievable by this method is 70 bytes,
+which is more than our usual minimum of 64 bytes.
+For that reason, the data frame sizes available for testing are 100 bytes
+(that allows room for eventually adding IPv6 ASTF tests),
+1518 bytes and 9000 bytes. There is no control over control packet sizes.
+
+Exactly as in UDP TPUT, ipackets and opackets counters are used for counting
+"transactions" (in fact packets).
+
+If packet loss occurs, there can be large transaction overlap, even if most
+ASTF programs finish eventually. This can lead to big duration stretching
+and somehow uneven rate of packets sent. This makes it hard to interpret
+MRR results (frequently MRR is below NDR for this reason),
+but NDR and PDR results tend to be stable enough.
+
+In 22.06, the "ACK from the receiving side" behavior changed,
+the receiving side started sending ACK sometimes
+also before receiving the full set of 5 data packets.
+If the previous profile is understood as a "single challenge, single response"
+where challenge (and also response) is sent as a burst of 5 data packets,
+the new profile uses "bursts" of 1 packet instead, but issues
+the challenge-response part 5 times sequentially
+(waiting for receiving the response before sending next challenge).
+This new profile happens to have the same overall packet count
+(when no re-transmissions are needed).
+Although it is possibly more taxing for TRex CPU,
+the results are comparable to the old traffic profile.
+
+## Ip4base tests
+
+Contrary to stateless traffic profiles, we do not have a simple limit
+that would guarantee TRex is able to send traffic at specified load.
+For that reason, we have added tests where "nat44ed" is replaced by "ip4base".
+Instead of NAT44ed processing, the tests set minimalistic IPv4 routes,
+so that packets are forwarded in both inside-to-outside and outside-to-inside
+directions.
+
+The packets arrive to server end of TRex with different source address&port
+than in NAT44ed tests (no translation to outside values is done with ip4base),
+but those are not specified in the stateful traffic profiles.
+The server end (as always) uses the received address&port as destination
+for outside-to-inside traffic. Therefore the same stateful traffic profile
+works for both NAT44ed and ip4base test (of the same scale).
+
+The NAT44ed results are displayed together with corresponding ip4base results.
+If they are similar, TRex is probably the bottleneck.
+If NAT44ed result is visibly smaller, it describes the real VPP performance.
--- /dev/null
+---
+bookToc: false
+title: "Packet Flow Ordering"
+weight: 9
+---
+
+# Packet Flow Ordering
+
+TRex Traffic Generator (TG) supports two main ways how to cover
+address space (on allowed ranges) in scale tests.
+
+In most cases only one field value (e.g. IPv4 destination address) is
+altered, in some cases two fields (e.g. IPv4 destination address and UDP
+destination port) are altered.
+
+## Incremental Ordering
+
+This case is simpler to implement and offers greater control.
+
+When changing two fields, they can be incremented synchronously, or one
+after another. In the latter case we can specify which one is
+incremented each iteration and which is incremented by "carrying over"
+only when the other "wraps around". This way also visits all
+combinations once before the "carry" field also wraps around.
+
+It is possible to use increments other than 1.
+
+## Randomized Ordering
+
+This case chooses each field value at random (from the allowed range).
+In case of two fields, they are treated independently.
+TRex allows to set random seed to get deterministic numbers.
+We use a different seed for each field and traffic direction.
+The seed has to be a non-zero number, we use 1, 2, 3, and so on.
+
+The seeded random mode in TRex requires a "limit" value,
+which acts as a cycle length limit (after this many iterations,
+the seed resets to its initial value).
+We use the maximal allowed limit value (computed as 2^24 - 1).
+
+Randomized profiles do not avoid duplicated values,
+and do not guarantee each possible value is visited,
+so it is not very useful for stateful tests.
--- /dev/null
+---
+bookToc: false
+title: "Packet Latency"
+weight: 8
+---
+
+# Packet Latency
+
+TRex Traffic Generator (TG) is used for measuring one-way latency in
+2-Node and 3-Node physical testbed topologies. TRex integrates
+[High Dynamic Range Histogram (HDRH)](http://hdrhistogram.org/)
+functionality and reports per packet latency distribution for latency
+streams sent in parallel to the main load packet streams.
+
+Following methodology is used:
+
+- Only NDRPDR test type measures latency and only after NDR and PDR
+ values are determined. Other test types do not involve latency
+ streams.
+- Latency is measured at different background load packet rates:
+
+ - No-Load: latency streams only.
+ - Low-Load: at 10% PDR.
+ - Mid-Load: at 50% PDR.
+ - High-Load: at 90% PDR.
+
+- Latency is measured for all tested packet sizes except IMIX due to
+ TRex TG restriction.
+- TG sends dedicated latency streams, one per direction, each at the
+ rate of 9 kpps at the prescribed packet size; these are sent in
+ addition to the main load streams.
+- TG reports Min/Avg/Max and HDRH latency values distribution per stream
+ direction, hence two sets of latency values are reported per test case
+ (marked as E-W and W-E).
+- +/- 1 usec is the measurement accuracy of TRex TG and the data in HDRH
+ latency values distribution is rounded to microseconds.
+- TRex TG introduces a (background) always-on Tx + Rx latency bias of 4
+ usec on average per direction resulting from TRex software writing and
+ reading packet timestamps on CPU cores. Quoted values are based on TG
+ back-to-back latency measurements.
+- Latency graphs are not smoothed, each latency value has its own
+ horizontal line across corresponding packet percentiles.
+- Percentiles are shown on X-axis using a logarithmic scale, so the
+ maximal latency value (ending at 100% percentile) would be in
+ infinity. The graphs are cut at 99.9999% (hover information still
+ lists 100%).
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "Per Thread Resources"
+weight: 2
+---
+
+# Per Thread Resources
+
+CSIT test framework is managing mapping of the following resources per
+thread:
+
+1. Cores, physical cores (pcores) allocated as pairs of sibling logical cores
+ (lcores) if server in HyperThreading/SMT mode, or as single lcores
+ if server not in HyperThreading/SMT mode. Note that if server's
+ processors are running in HyperThreading/SMT mode sibling lcores are
+ always used.
+2. Receive Queues (RxQ), packet receive queues allocated on each
+ physical and logical interface tested.
+3. Transmit Queues(TxQ), packet transmit queues allocated on each
+ physical and logical interface tested.
+
+Approach to mapping per thread resources depends on the application/DUT
+tested (VPP or DPDK apps) and associated thread types, as follows:
+
+1. Data-plane workers, used for data-plane packet processing, when no
+ feature workers present.
+
+ - Cores: data-plane workers are typically tested in 1, 2 and 4 pcore
+ configurations, running on single lcore per pcore or on sibling
+ lcores per pcore. Result is a set of {T}t{C}c thread-core
+ configurations, where{T} stands for a total number of threads
+ (lcores), and {C} for a total number of pcores. Tested
+ configurations are encoded in CSIT test case names,
+ e.g. "1c", "2c", "4c", and test tags "2T1C"(or "1T1C"), "4T2C"
+ (or "2T2C"), "8T4C" (or "4T4C").
+ - Interface Receive Queues (RxQ): as of CSIT-2106 release, number of
+ RxQs used on each physical or virtual interface is equal to the
+ number of data-plane workers. In other words each worker has a
+ dedicated RxQ on each interface tested. This ensures packet
+ processing load to be equal for each worker, subject to RSS flow
+ load balancing efficacy. Note: Before CSIT-2106 total number of
+ RxQs across all interfaces of specific type was equal to the
+ number of data-plane workers.
+ - Interface Transmit Queues (TxQ): number of TxQs used on each
+ physical or virtual interface is equal to the number of data-plane
+ workers. In other words each worker has a dedicated TxQ on each
+ interface tested.
+ - Applies to VPP and DPDK Testpmd and L3Fwd.
+
+2. Data-plane and feature workers (e.g. IPsec async crypto workers), the
+ latter dedicated to specific feature processing.
+
+ - Cores: data-plane and feature workers are tested in 2, 3 and 4
+ pcore configurations, running on single lcore per pcore or on
+ sibling lcores per pcore. This results in a two sets of
+ thread-core combinations separated by "-", {T}t{C}c-{T}t{C}c, with
+ the leading set denoting total number of threads (lcores) and
+ pcores used for data-plane workers, and the trailing set denoting
+ total number of lcores and pcores used for feature workers.
+ Accordingly, tested configurations are encoded in CSIT test case
+ names, e.g. "1c-1c", "1c-2c", "1c-3c", and test tags "2T1C_2T1C"
+ (or "1T1C_1T1C"), "2T1C_4T2C"(or "1T1C_2T2C"), "2T1C_6T3C"
+ (or "1T1C_3T3C").
+ - RxQ and TxQ: no RxQs and no TxQs are used by feature workers.
+ - Applies to VPP only.
+
+3. Management/main worker, control plane and management.
+
+ - Cores: single lcore.
+ - RxQ: not used (VPP default behaviour).
+ - TxQ: single TxQ per interface, allocated but not used
+ (VPP default behaviour).
+ - Applies to VPP only.
+
+## VPP Thread Configuration
+
+Mapping of cores and RxQs to VPP data-plane worker threads is done in
+the VPP startup.conf during test suite setup:
+
+1. `corelist-workers <list_of_cores>`: List of logical cores to run VPP
+ data-plane workers and feature workers. The actual lcores'
+ allocations depends on HyperThreading/SMT server configuration and
+ per test core configuration.
+
+ - For tests without feature workers, by default, all CPU cores
+ configured in startup.conf are used for data-plane workers.
+ - For tests with feature workers, CSIT code distributes lcores across
+ data-plane and feature workers.
+
+2. `num-rx-queues <value>`: Number of Rx queues used per interface.
+
+Mapping of TxQs to VPP data-plane worker threads uses the default VPP
+setting of one TxQ per interface per data-plane worker.
+
+## DPDK Thread Configuration
+
+Mapping of cores and RxQs to DPDK Testpmd/L3Fwd data-plane worker
+threads is done in the startup CLI:
+
+1. `-l <list_of_cores>` - List of logical cores to run DPDK
+ application.
+2. `nb-cores=<N>` - Number of forwarding cores.
+3. `rxq=<N>` - Number of Rx queues used per interface.
--- /dev/null
+---
+bookToc: false
+title: "Reconfiguration Tests"
+weight: 16
+---
+
+# Reconfiguration Tests
+
+## Overview
+
+Reconf tests are designed to measure the impact of VPP re-configuration
+on data plane traffic.
+While VPP takes some measures against the traffic being
+entirely stopped for a prolonged time,
+the immediate forwarding rate varies during the re-configuration,
+as some configurations steps need the active dataplane worker threads
+to be stopped temporarily.
+
+As the usual methods of measuring throughput need multiple trial measurements
+with somewhat long durations, and the re-configuration process can also be long,
+finding an offered load which would result in zero loss
+during the re-configuration process would be time-consuming.
+
+Instead, reconf tests first find a througput value (lower bound for NDR)
+without re-configuration, and then maintain that ofered load
+during re-configuration. The measured loss count is then assumed to be caused
+by the re-configuration process. The result published by reconf tests
+is the effective blocked time, that is
+the loss count divided by the offered load.
+
+## Current Implementation
+
+Each reconf suite is based on a similar MLRsearch performance suite.
+
+MLRsearch parameters are changed to speed up the throughput discovery.
+For example, PDR is not searched for, and the final trial duration is shorter.
+
+The MLRsearch suite has to contain a configuration parameter
+that can be scaled up, e.g. number of tunnels or number of service chains.
+Currently, only increasing the scale is supported
+as the re-configuration operation. In future, scale decrease
+or other operations can be implemented.
+
+The traffic profile is not changed, so the traffic present is processed
+only by the smaller scale configuration. The added tunnels / chains
+are not targetted by the traffic.
+
+For the re-configuration, the same Robot Framework and Python libraries
+are used, as were used in the initial configuration, with the exception
+of the final calls that do not interact with VPP (e.g. starting
+virtual machines) being skipped to reduce the test overall duration.
+
+## Discussion
+
+Robot Framework introduces a certain overhead, which may affect timing
+of individual VPP API calls, which in turn may affect
+the number of packets lost.
+
+The exact calls executed may contain unnecessary info dumps, repeated commands,
+or commands which change a value that do not need to be changed (e.g. MTU).
+Thus, implementation details are affecting the results, even if their effect
+on the corresponding MLRsearch suite is negligible.
+
+The lower bound for NDR is the only value safe to be used when zero packets lost
+are expected without re-configuration. But different suites show different
+"jitter" in that value. For some suites, the lower bound is not tight,
+allowing full NIC buffers to drain quickly between worker pauses.
+For other suites, lower bound for NDR still has quite a large probability
+of non-zero packet loss even without re-configuration.
--- /dev/null
+---
+bookCollapseSection: true
+bookFlatSection: false
+title: "Root Cause Analysis"
+weight: 20
+---
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "Per-patch performance tests"
+weight: 1
+---
+
+# Per-patch performance tests
+
+Updated for CSIT git commit id: 72b45cfe662107c8e1bb549df71ba51352a898ee.
+
+A methodology similar to trending analysis is used for comparing performance
+before a DUT code change is merged. This can act as a verify job to disallow
+changes which would decrease performance without a good reason.
+
+## Existing jobs
+
+VPP is the only project currently using such jobs.
+They are not started automatically, must be triggered on demand.
+They allow full tag expressions, but some tags are enforced (such as MRR).
+
+There are jobs available for multiple types of testbeds,
+based on various processors.
+Their Gerrit triggers words are of the form "perftest-{node_arch}"
+where the node_arch combinations currently supported are:
+2n-clx, 2n-tx2, 2n-zn2, 3n-tsh.
+
+## Test selection
+
+Gerrit trigger line without any additional arguments selects
+a small set of test cases to run.
+If additional arguments are added to the Gerrit trigger, they are treated
+as Robot tag expressions to select tests to run.
+While very flexible, this method of test selection also allows the user
+to accidentally select too high number of tests, blocking the testbed for days.
+
+What follows is a list of explanations and recommendations
+to help users to select the minimal set of tests cases.
+
+### Verify cycles
+
+When Gerrit schedules multiple jobs to run for the same patch set,
+it waits until all runs are complete.
+While it is waiting, it is possible to trigger more jobs
+(adding runs to the set Gerrit is waiting for), but it is not possible
+to trigger more runs for the same job, until Gerrit is done waiting.
+After Gerrit is done waiting, it becames possible to trigger
+the same job again.
+
+Example. User triggers one set of tests on 2n-icx and immediately
+also triggers other set of tests on 3n-icx. Then the user notices
+2n-icx run end early because of a typo in tag expression.
+When the user tries to re-trigger 2n-icx (with fixed tag expression),
+that comment gets ignored by Jenkins.
+Only when 3n-icx job finishes, the user can trigger 2n-icx.
+
+### One comment many jobs
+
+In the past, the CSIT code which parses for perftest trigger comments
+was buggy, which lead to bad behavior (as in selection all performance test,
+because "perftest" is also a robot tag) when user included multiple
+perftest trigger words in the same comment.
+
+The worst bugs were fixed since then, but it is still recommended
+to use just one trigger word per Gerrit comment, just to be safe.
+
+### Multiple test cases in run
+
+While Robot supports OR operator, it does not support parentheses,
+so the OR operator is not very useful. It is recommended
+to use space instead of OR operator.
+
+Example template:
+perftest-2n-icx {tag_expression_1} {tag_expression_2}
+
+See below for more concrete examples.
+
+### Suite tags
+
+Traditionally, CSIT maintains broad Robot tags that can be used to select tests.
+
+But it is not recommended to use them for test selection,
+as it is not that easy to determine how many test cases are selected.
+
+The recommended way is to look into CSIT repository first,
+and locate a specific suite the user is interested in,
+and use its suite tag. For example, "ethip4-ip4base" is a suite tag
+selecting just one suite in CSIT git repository,
+avoiding all scale, container, and other simialr variants.
+
+Note that CSIT uses "autogen" code generator,
+so the robot running in Jenkins has access to more suites
+than visible just by looking into CSIT git repository,
+so suite tag is not enough to select even the intended suite,
+and user still probably wants to narrow down
+to a single test case within a suite.
+
+### Fully specified tag expressions
+
+Here is one template to select a single test case:
+{test_type}AND{nic_model}AND{nic_driver}AND{cores}AND{frame_size}AND{suite_tag}
+where the variables are all lower case (so AND operator stands out).
+
+Currently only one test type is supported by the performance comparison jobs:
+"mrr".
+The nic_driver options depend on nic_model. For Intel cards "drv_avf" (AVF plugin)
+and "drv_vfio_pci" (DPDK plugin) are popular, for Mellanox "drv_rdma_core".
+Currently, the performance using "drv_af_xdp" is not reliable enough, so do not use it
+unless you are specifically testing for AF_XDP.
+
+The most popular nic_model is "nic_intel-xxv710", but that is not available
+on all testbed types.
+It is safe to use "1c" for cores (unless you are suspection multi-core performance
+is affected differently) and "64b" for frame size ("78b" for ip6
+and more for dot1q and other encapsulated traffic;
+"1518b" is popular for ipsec and other payload-bound tests).
+
+As there are more test cases than CSIT can periodically test,
+it is possible to encounter an old test case that currently fails.
+To avoid that, you can look at "job spec" files we use for periodic testing,
+for example
+[this one](https://github.com/FDio/csit/blob/master/resources/job_specs/report_iterative/2n-icx/vpp-mrr-00.md).
+
+### Shortening triggers
+
+Advanced users may use the following tricks to avoid writing long trigger comments.
+
+Robot supports glob matching, which can be used to select multiple suite tags at once.
+
+Not specifying one of 6 parts of the recommended expression pattern
+will select all available options. For example not specifying nic_driver
+for nic_intel-xxv710 will select all 3 applicable drivers.
+You can use NOT operator to reject some options (e.g. NOTdrv_af_xdp),
+but beware, with NOT the order matters:
+tag1ANDtag2NOTtag3 is not the same as tag1NOTtag3ANDtag2,
+the latter is evaluated as tag1AND(NOT(tag3ANDtag2)).
+
+Beware when not specifying nic_model. As a precaution,
+CSIT code will insert the defailt NIC model for the tetsbed used.
+Example: Specifying drv_rdma_core without specifying nic_model
+will fail, as the default nic_model is nic_intel-xxv710
+which does not support RDMA core driver.
+
+### Complete example
+
+A user wants to test a VPP change which may affect load balance whith bonding.
+Searching tag documentation for "bonding" finds LBOND tag and its variants.
+Searching CSIT git repository (directory tests/) finds 8 suite files,
+all suited only for 3-node testbeds.
+All suites are using vhost, but differ by the forwarding app inside VM
+(DPDK or VPP), by the forwarding mode of VPP acting as host level vswitch
+(MAC learning or cross connect), and by the number of DUT1-DUT2 links
+available (1 or 2).
+
+As not all NICs and testbeds offer enogh ports for 2 parallel DUT-DUT links,
+the user looks at
+[testbed specifications](https://github.com/FDio/csit/tree/master/topologies/available)
+and finds that only xxv710 NIC on 3n-icx testbed matches the requirements.
+Quick look into the suites confirm the smallest frame size is 64 bytes
+(despite DOT1Q robot tag, as the encapsulation does not happen on TG-DUT links).
+It is ok to use just 1 physical core, as 3n-icx has hyperthreading enabled,
+so VPP vswitch will use 2 worker threads.
+
+The user decides the vswitch forwarding mode is not important
+(so choses cross connect as that has less CPU overhead),
+but wants to test both NIC drivers (not AF_XDP), both apps in VM,
+and both 1 and 2 parallel links.
+
+After shortening, this is the trigger comment fianlly used:
+perftest-3n-icx mrrANDnic_intel-x710AND1cAND64bAND?lbvpplacp-dot1q-l2xcbase-eth-2vhostvr1024-1vm*NOTdrv_af_xdp
+
+## Basic operation
+
+The job builds VPP .deb packages for both the patch under test
+(called "current") and its parent patch (called "parent").
+
+For each test (from a set defined by tag expression),
+both builds are subjected to several trial measurements (BMRR).
+Measured samples are grouped to "parent" sequence,
+followed by "current" sequence. The same Minimal Description Length
+algorithm as in trending is used to decide whether it is one big group,
+or two smaller gropus. If it is one group, a "normal" result
+is declared for the test. If it is two groups, and current average
+is less then parent average, the test is declared a regression.
+If it is two groups and current average is larger or equal,
+the test is declared a progression.
+
+The whole job fails (giving -1) if some trial measurement failed,
+or if any test was declared a regression.
+
+## Temporary specifics
+
+The Minimal Description Length analysis is performed by
+CSIT code equivalent to jumpavg-0.1.3 library available on PyPI.
+
+In hopes of strengthening of signal (code performance) compared to noise
+(all other factors influencing the measured values), several workarounds
+are applied.
+
+In contrast to trending, trial duration is set to 10 seconds,
+and only 5 samples are measured for each build.
+Both parameters are set in ci-management.
+
+This decreases sensitivity to regressions, but also decreases
+probability of false positives.
+
+## Console output
+
+The following information as visible towards the end of Jenkins console output,
+repeated for each analyzed test.
+
+The original 5 values are visible in order they were measured.
+The 5 values after processing are also visible in output,
+this time sorted by value (so people can see minimum and maximum).
+
+The next output is difference of averages. It is the current average
+minus the parent average, expressed as percentage of the parent average.
+
+The next three outputs contain the jumpavg representation
+of the two groups and a combined group.
+Here, "bits" is the description length; for "current" sequence
+it includes effect from "parent" average value
+(jumpavg-0.1.3 penalizes sequences with too close averages).
+
+Next, a sentence describing which grouping description is shorter,
+and by how much bits.
+Finally, the test result classification is visible.
+
+The algorithm does not track test case names,
+so test cases are indexed (from 0).
--- /dev/null
+---
+bookToc: false
+title: "Suite Generation"
+weight: 19
+---
+
+# Suite Generation
+
+CSIT uses robot suite files to define tests.
+However, not all suite files available for Jenkins jobs
+(or manually started bootstrap scripts) are present in CSIT git repository.
+They are generated only when needed.
+
+## Autogen Library
+
+There is a code generation layer implemented as Python library called "autogen",
+called by various bash scripts.
+
+It generates the full extent of CSIT suites, using the ones in git as templates.
+
+## Sources
+
+The generated suites (and their contents) are affected by multiple information
+sources, listed below.
+
+### Git Suites
+
+The suites present in git repository act as templates for generating suites.
+One of autogen design principles is that any template suite should also act
+as a full suite (no placeholders).
+
+In practice, autogen always re-creates the template suite with exactly
+the same content, it is one of checks that autogen works correctly.
+
+### Regenerate Script
+
+Not all suites present in CSIT git repository act as template for autogen.
+The distinction is on per-directory level. Directories with
+regenerate_testcases.py script usually consider all suites as templates
+(unless possibly not included by the glob patten in the script).
+
+The script also specifies minimal frame size, indirectly, by specifying protocol
+(protocol "ip4" is the default, leading to 64B frame size).
+
+### Constants
+
+Values in Constants.py are taken into consideration when generating suites.
+The values are mostly related to different NIC models and NIC drivers.
+
+### Python Code
+
+Python code in resources/libraries/python/autogen contains several other
+information sources.
+
+#### Testcase Templates
+
+The test case part of template suite is ignored, test case lines
+are created according to text templates in Testcase.py file.
+
+#### Testcase Argument Lists
+
+Each testcase template has different number of "arguments", e.g. values
+to put into various placeholders. Different test types need different
+lists of the argument values, the lists are in regenerate_glob method
+in Regenerator.py file.
+
+#### Iteration Over Values
+
+Python code detects the test type (usually by substrings of suite file name),
+then iterates over different quantities based on type.
+For example, only ndrpdr suite templates generate other types (mrr and soak).
+
+#### Hardcoded Exclusions
+
+Some combinations of values are known not to work, so they are excluded.
+Examples: Density tests for too much CPUs; IMIX for ASTF.
+
+## Non-Sources
+
+Some information sources are available in CSIT repository,
+but do not affect the suites generated by autogen.
+
+### Testbeds
+
+Overall, no information visible in topology yaml files is taken into account
+by autogen.
+
+#### Testbed Architecture
+
+Historically, suite files are agnostic to testbed architecture, e.g. ICX or ALT.
+
+#### Testbed Size
+
+Historically, 2-node and 3-node suites have diferent names, and while
+most of the code is common, the differences are not always simple enough.
+Autogen treat 2-node and 3-node suites as independent templates.
+
+TRex suites are intended for a 1-node circuit of otherwise 2-node or 3-node
+testbeds, so they support all 3 robot tags.
+They are also detected and treated differently by autogen,
+mainly because they need different testcase arguments (no CPU count).
+Autogen does nothing specifically related to the fact they should run
+only in testbeds/NICs with TG-TG line available.
+
+#### Other Topology Info
+
+Some bonding tests need two (parallel) links between DUTs.
+Autogen does not care, as suites are agnostic.
+Robot tag marks the difference, but the link presence is not explicitly checked.
+
+### Job specs
+
+Information in job spec files depend on generated suites (not the other way).
+Autogen should generate more suites, as job spec is limited by time budget.
+More suites should be available for manually triggered verify jobs,
+so autogen covers that.
+
+### Bootstrap Scripts
+
+Historically, bootstrap scripts perform some logic,
+perhaps adding exclusion options to Robot invocation
+(e.g. skipping testbed+NIC combinations for tests that need parallel links).
+
+Once again, the logic here relies on what autogen generates,
+autogen does not look into bootstrap scripts.
--- /dev/null
+---
+bookToc: false
+title: "Telemetry"
+weight: 20
+---
+
+# Telemetry
+
+OpenMetrics specifies the de-facto standard for transmitting cloud-native
+metrics at scale, with support for both text representation and Protocol
+Buffers.
+
+## RFC
+
+- RFC2119
+- RFC5234
+- RFC8174
+- draft-richih-opsawg-openmetrics-00
+
+## Reference
+
+[OpenMetrics](https://github.com/OpenObservability/OpenMetrics/blob/master/specification/OpenMetrics.md)
+
+## Metric Types
+
+- Gauge
+- Counter
+- StateSet
+- Info
+- Histogram
+- GaugeHistogram
+- Summary
+- Unknown
+
+Telemetry module in CSIT currently support only Gauge, Counter and Info.
+
+## Anatomy of CSIT telemetry implementation
+
+Existing implementation consists of several measurment building blocks:
+the main measuring block running search algorithms (MLR, PLR, SOAK, MRR, ...),
+the latency measuring block and the several telemetry blocks with or without
+traffic running on a background.
+
+The main measuring block must not be interrupted by any read operation that can
+impact data plane traffic processing during throughput search algorithm. Thus
+operational reads are done before (pre-stat) and after (post-stat) that block.
+
+Some operational reads must be done while traffic is running and usually
+consists of two reads (pre-run-stat, post-run-stat) with defined delay between
+them.
+
+## MRR measurement
+
+ traffic_start(r=mrr) traffic_stop |< measure >|
+ | | | (r=mrr) |
+ | pre_run_stat post_run_stat | pre_stat | | post_stat
+ | | | | | | | |
+ --o--------o---------------o---------o-------o--------+-------------------+------o------------>
+ t
+
+ Legend:
+ - pre_run_stat
+ - vpp-clear-runtime
+ - post_run_stat
+ - vpp-show-runtime
+ - bash-perf-stat // if extended_debug == True
+ - pre_stat
+ - vpp-clear-stats
+ - vpp-enable-packettrace // if extended_debug == True
+ - vpp-enable-elog
+ - post_stat
+ - vpp-show-stats
+ - vpp-show-packettrace // if extended_debug == True
+ - vpp-show-elog
+
+
+ |< measure >|
+ | (r=mrr) |
+ | |
+ |< traffic_trial0 >|< traffic_trial1 >|< traffic_trialN >|
+ | (i=0,t=duration) | (i=1,t=duration) | (i=N,t=duration) |
+ | | | |
+ --o------------------------o------------------------o------------------------o--->
+ t
+
+
+## MLR measurement
+
+ |< measure >| traffic_start(r=pdr) traffic_stop traffic_start(r=ndr) traffic_stop |< [ latency ] >|
+ | (r=mlr) | | | | | | .9/.5/.1/.0 |
+ | | | pre_run_stat post_run_stat | | pre_run_stat post_run_stat | | |
+ | | | | | | | | | | | |
+ --+-------------------+----o--------o---------------o---------o--------------o--------o---------------o---------o------------[---------------------]--->
+ t
+
+ Legend:
+ - pre_run_stat
+ - vpp-clear-runtime
+ - post_run_stat
+ - vpp-show-runtime
+ - bash-perf-stat // if extended_debug == True
+ - pre_stat
+ - vpp-clear-stats
+ - vpp-enable-packettrace // if extended_debug == True
+ - vpp-enable-elog
+ - post_stat
+ - vpp-show-stats
+ - vpp-show-packettrace // if extended_debug == True
+ - vpp-show-elog
+
+
+## MRR measurement
+
+ traffic_start(r=mrr) traffic_stop |< measure >|
+ | | | (r=mrr) |
+ | |< stat_runtime >| | stat_pre_trial | | stat_post_trial
+ | | | | | | | |
+ ----o---+--------------------------+---o-------------o------------+-------------------+-----o------------->
+ t
+
+ Legend:
+ - stat_runtime
+ - vpp-runtime
+ - stat_pre_trial
+ - vpp-clear-stats
+ - vpp-enable-packettrace // if extended_debug == True
+ - stat_post_trial
+ - vpp-show-stats
+ - vpp-show-packettrace // if extended_debug == True
+
+
+ |< measure >|
+ | (r=mrr) |
+ | |
+ |< traffic_trial0 >|< traffic_trial1 >|< traffic_trialN >|
+ | (i=0,t=duration) | (i=1,t=duration) | (i=N,t=duration) |
+ | | | |
+ --o------------------------o------------------------o------------------------o--->
+ t
+
+
+ |< stat_runtime >|
+ | |
+ |< program0 >|< program1 >|< programN >|
+ | (@=params) | (@=params) | (@=params) |
+ | | | |
+ --o------------------------o------------------------o------------------------o--->
+ t
+
+
+## MLR measurement
+
+ |< measure >| traffic_start(r=pdr) traffic_stop traffic_start(r=ndr) traffic_stop |< [ latency ] >|
+ | (r=mlr) | | | | | | .9/.5/.1/.0 |
+ | | | |< stat_runtime >| | | |< stat_runtime >| | | |
+ | | | | | | | | | | | |
+ --+-------------------+-----o---+--------------------------+---o--------------o---+--------------------------+---o-----------[---------------------]--->
+ t
+
+ Legend:
+ - stat_runtime
+ - vpp-runtime
+ - stat_pre_trial
+ - vpp-clear-stats
+ - vpp-enable-packettrace // if extended_debug == True
+ - stat_post_trial
+ - vpp-show-stats
+ - vpp-show-packettrace // if extended_debug == True
--- /dev/null
+---
+bookToc: false
+title: "Terminology"
+weight: 1
+---
+
+# Terminology
+
+- **Frame size**: size of an Ethernet Layer-2 frame on the wire, including
+ any VLAN tags (dot1q, dot1ad) and Ethernet FCS, but excluding Ethernet
+ preamble and inter-frame gap. Measured in Bytes.
+- **Packet size**: same as frame size, both terms used interchangeably.
+- **Inner L2 size**: for tunneled L2 frames only, size of an encapsulated
+ Ethernet Layer-2 frame, preceded with tunnel header, and followed by
+ tunnel trailer. Measured in Bytes.
+- **Inner IP size**: for tunneled IP packets only, size of an encapsulated
+ IPv4 or IPv6 packet, preceded with tunnel header, and followed by
+ tunnel trailer. Measured in Bytes.
+- **Device Under Test (DUT)**: In software networking, "device" denotes a
+ specific piece of software tasked with packet processing. Such device
+ is surrounded with other software components (such as operating system
+ kernel). It is not possible to run devices without also running the
+ other components, and hardware resources are shared between both. For
+ purposes of testing, the whole set of hardware and software components
+ is called "System Under Test" (SUT). As SUT is the part of the whole
+ test setup performance of which can be measured with RFC2544, using
+ SUT instead of RFC2544 DUT. Device under test
+ (DUT) can be re-introduced when analyzing test results using whitebox
+ techniques, but this document sticks to blackbox testing.
+- **System Under Test (SUT)**: System under test (SUT) is a part of the
+ whole test setup whose performance is to be benchmarked. The complete
+ methodology contains other parts, whose performance is either already
+ established, or not affecting the benchmarking result.
+- **Bi-directional throughput tests**: involve packets/frames flowing in
+ both east-west and west-east directions over every tested interface of
+ SUT/DUT. Packet flow metrics are measured per direction, and can be
+ reported as aggregate for both directions (i.e. throughput) and/or
+ separately for each measured direction (i.e. latency). In most cases
+ bi-directional tests use the same (symmetric) load in both directions.
+- **Uni-directional throughput tests**: involve packets/frames flowing in
+ only one direction, i.e. either east-west or west-east direction, over
+ every tested interface of SUT/DUT. Packet flow metrics are measured
+ and are reported for measured direction.
+- **Packet Loss Ratio (PLR)**: ratio of packets received relative to packets
+ transmitted over the test trial duration, calculated using formula:
+ PLR = ( pkts_transmitted - pkts_received ) / pkts_transmitted.
+ For bi-directional throughput tests aggregate PLR is calculated based
+ on the aggregate number of packets transmitted and received.
+- **Packet Throughput Rate**: maximum packet offered load DUT/SUT forwards
+ within the specified Packet Loss Ratio (PLR). In many cases the rate
+ depends on the frame size processed by DUT/SUT. Hence packet
+ throughput rate MUST be quoted with specific frame size as received by
+ DUT/SUT during the measurement. For bi-directional tests, packet
+ throughput rate should be reported as aggregate for both directions.
+ Measured in packets-per-second (pps) or frames-per-second (fps),
+ equivalent metrics.
+- **Bandwidth Throughput Rate**: a secondary metric calculated from packet
+ throughput rate using formula: bw_rate = pkt_rate * (frame_size +
+ L1_overhead) * 8, where L1_overhead for Ethernet includes preamble (8
+ Bytes) and inter-frame gap (12 Bytes). For bi-directional tests,
+ bandwidth throughput rate should be reported as aggregate for both
+ directions. Expressed in bits-per-second (bps).
+- **Non Drop Rate (NDR)**: maximum packet/bandwith throughput rate sustained
+ by DUT/SUT at PLR equal zero (zero packet loss) specific to tested
+ frame size(s). MUST be quoted with specific packet size as received by
+ DUT/SUT during the measurement. Packet NDR measured in
+ packets-per-second (or fps), bandwidth NDR expressed in
+ bits-per-second (bps).
+- **Partial Drop Rate (PDR)**: maximum packet/bandwith throughput rate
+ sustained by DUT/SUT at PLR greater than zero (non-zero packet loss)
+ specific to tested frame size(s). MUST be quoted with specific packet
+ size as received by DUT/SUT during the measurement. Packet PDR
+ measured in packets-per-second (or fps), bandwidth PDR expressed in
+ bits-per-second (bps).
+- **Maximum Receive Rate (MRR)**: packet/bandwidth rate regardless of PLR
+ sustained by DUT/SUT under specified Maximum Transmit Rate (MTR)
+ packet load offered by traffic generator. MUST be quoted with both
+ specific packet size and MTR as received by DUT/SUT during the
+ measurement. Packet MRR measured in packets-per-second (or fps),
+ bandwidth MRR expressed in bits-per-second (bps).
+- **Trial**: a single measurement step.
+- **Trial duration**: amount of time over which packets are transmitted and
+ received in a single measurement step.
--- /dev/null
+---
+bookCollapseSection: true
+bookFlatSection: false
+title: "Trending Methodology"
+weight: 22
+---
\ No newline at end of file
--- /dev/null
+---
+bookFlatSection: true
+title: "Overview"
+weight: 1
+---
+
+# Overview
+
+This document describes a high-level design of a system for continuous
+performance measuring, trending and change detection for FD.io VPP SW
+data plane (and other performance tests run within CSIT sub-project).
--- /dev/null
+---
+bookFlatSection: true
+title: "Trending Analysis"
+weight: 2
+---
+
+# Trend Analysis
+
+All measured performance trend data is treated as time-series data
+that is modeled as a concatenation of groups,
+within each group the samples come (independently) from
+the same normal distribution (with some center and standard deviation).
+
+Center of the normal distribution for the group (equal to population average)
+is called a trend for the group.
+All the analysis is based on finding the right partition into groups
+and comparing their trends.
+
+## Anomalies in graphs
+
+In graphs, the start of the following group is marked as a regression (red
+circle) or progression (green circle), if the new trend is lower (or higher
+respectively) then the previous group's.
+
+## Implementation details
+
+### Partitioning into groups
+
+While sometimes the samples within a group are far from being distributed
+normally, currently we do not have a better tractable model.
+
+Here, "sample" should be the result of single trial measurement, with group
+boundaries set only at test run granularity. But in order to avoid detecting
+causes unrelated to VPP performance, the current presentation takes average of
+all trials within the run as the sample. Effectively, this acts as a single
+trial with aggregate duration.
+
+Performance graphs show the run average as a dot (not all individual trial
+results).
+
+The group boundaries are selected based on `Minimum Description Length`[^1].
+
+### Minimum Description Length
+
+`Minimum Description Length`[^1] (MDL) is a particular formalization
+of `Occam's razor`[^2] principle.
+
+The general formulation mandates to evaluate a large set of models,
+but for anomaly detection purposes, it is useful to consider
+a smaller set of models, so that scoring and comparing them is easier.
+
+For each candidate model, the data should be compressed losslessly,
+which includes model definitions, encoded model parameters,
+and the raw data encoded based on probabilities computed by the model.
+The model resulting in shortest compressed message is the "the" correct model.
+
+For our model set (groups of normally distributed samples),
+we need to encode group length (which penalizes too many groups),
+group average (more on that later), group stdev and then all the samples.
+
+Luckily, the "all the samples" part turns out to be quite easy to compute.
+If sample values are considered as coordinates in (multi-dimensional)
+Euclidean space, fixing stdev means the point with allowed coordinates
+lays on a sphere. Fixing average intersects the sphere with a (hyper)-plane,
+and Gaussian probability density on the resulting sphere is constant.
+So the only contribution is the "area" of the sphere, which only depends
+on the number of samples and stdev.
+
+A somehow ambiguous part is in choosing which encoding
+is used for group size, average and stdev.
+Different encodings cause different biases to large or small values.
+In our implementation we have chosen probability density
+corresponding to uniform distribution (from zero to maximal sample value)
+for stdev and average of the first group,
+but for averages of subsequent groups we have chosen a distribution
+which discourages delimiting groups with averages close together.
+
+Our implementation assumes that measurement precision is 1.0 pps.
+Thus it is slightly wrong for trial durations other than 1.0 seconds.
+Also, all the calculations assume 1.0 pps is totally negligible,
+compared to stdev value.
+
+The group selection algorithm currently has no parameters,
+all the aforementioned encodings and handling of precision is hard-coded.
+In principle, every group selection is examined, and the one encodable
+with least amount of bits is selected.
+As the bit amount for a selection is just sum of bits for every group,
+finding the best selection takes number of comparisons
+quadratically increasing with the size of data,
+the overall time complexity being probably cubic.
+
+The resulting group distribution looks good
+if samples are distributed normally enough within a group.
+But for obviously different distributions (for example
+`bimodal distribution`[^3]) the groups tend to focus on less relevant factors
+(such as "outlier" density).
+
+## Common Patterns
+
+When an anomaly is detected, it frequently falls into few known patterns,
+each having its typical behavior over time.
+
+We are going to describe the behaviors,
+as they motivate our choice of trend compliance metrics.
+
+### Sample time and analysis time
+
+But first we need to distinguish two roles time plays in analysis,
+so it is more clear which role we are referring to.
+
+Sample time is the more obvious one.
+It is the time the sample is generated.
+It is the start time or the end time of the Jenkins job run,
+does not really matter which (parallel runs are disabled,
+and length of gap between samples does not affect metrics).
+
+Analysis time is the time the current analysis is computed.
+Again, the exact time does not usually matter,
+what matters is how many later (and how fewer earlier) samples
+were considered in the computation.
+
+For some patterns, it is usual for a previously reported
+anomaly to "vanish", or previously unseen anomaly to "appear late",
+as later samples change which partition into groups is more probable.
+
+Dashboard and graphs are always showing the latest analysis time,
+the compliance metrics are using earlier sample time
+with the same latest analysis time.
+
+Alerting e-mails use the latest analysis time at the time of sending,
+so the values reported there are likely to be different
+from the later analysis time results shown in dashboard and graphs.
+
+### Ordinary regression
+
+The real performance changes from previously stable value
+into a new stable value.
+
+For medium to high magnitude of the change, one run
+is enough for anomaly detection to mark this regression.
+
+Ordinary progressions are detected in the same way.
+
+### Small regression
+
+The real performance changes from previously stable value
+into a new stable value, but the difference is small.
+
+For the anomaly detection algorithm, this change is harder to detect,
+depending on the standard deviation of the previous group.
+
+If the new performance value stays stable, eventually
+the detection algorithm is able to detect this anomaly
+when there are enough samples around the new value.
+
+If the difference is too small, it may remain undetected
+(as new performance change happens, or full history of samples
+is still not enough for the detection).
+
+Small progressions have the same behavior.
+
+### Reverted regression
+
+This pattern can have two different causes.
+We would like to distinguish them, but that is usually
+not possible to do just by looking at the measured values (and not telemetry).
+
+In one cause, the real DUT performance has changed,
+but got restored immediately.
+In the other cause, no real performance change happened,
+just some temporary infrastructure issue
+has caused a wrong low value to be measured.
+
+For small measured changes, this pattern may remain undetected.
+For medium and big measured changes, this is detected when the regression
+happens on just the last sample.
+
+For big changes, the revert is also immediately detected
+as a subsequent progression. The trend is usually different
+from the previously stable trend (as the two population averages
+are not likely to be exactly equal), but the difference
+between the two trends is relatively small.
+
+For medium changes, the detection algorithm may need several new samples
+to detect a progression (as it dislikes single sample groups),
+in the meantime reporting regressions (difference decreasing
+with analysis time), until it stabilizes the same way as for big changes
+(regression followed by progression, small difference
+between the old stable trend and last trend).
+
+As it is very hard for a fault code or an infrastructure issue
+to increase performance, the opposite (temporary progression)
+almost never happens.
+
+### Summary
+
+There is a trade-off between detecting small regressions
+and not reporting the same old regressions for a long time.
+
+For people reading e-mails, a sudden regression with a big number of samples
+in the last group means this regression was hard for the algorithm to detect.
+
+If there is a big regression with just one run in the last group,
+we are not sure if it is real, or just a temporary issue.
+It is useful to wait some time before starting an investigation.
+
+With decreasing (absolute value of) difference, the number of expected runs
+increases. If there is not enough runs, we still cannot distinguish
+real regression from temporary regression just from the current metrics
+(although humans frequently can tell by looking at the graph).
+
+When there is a regression or progression with just a small difference,
+it is probably an artifact of a temporary regression.
+Not worth examining, unless temporary regressions happen somewhat frequently.
+
+It is not easy for the metrics to locate the previous stable value,
+especially if multiple anomalies happened in the last few weeks.
+It is good to compare last trend with long term trend maximum,
+as it highlights the difference between "now" and "what could be".
+It is good to exclude last week from the trend maximum,
+as including the last week would hide all real progressions.
+
+[^1]: [Minimum Description Length](https://en.wikipedia.org/wiki/Minimum_description_length)
+[^2]: [Occam's razor](https://en.wikipedia.org/wiki/Occam%27s_razor)
+[^3]: [bimodal distribution](https://en.wikipedia.org/wiki/Bimodal_distribution)
--- /dev/null
+---
+bookFlatSection: true
+title: "Trending Presentation"
+weight: 3
+---
+
+# Trend Presentation
+
+## Failed tests
+
+The Failed tests tables list the tests which failed during the last test run.
+Separate tables are generated for each testbed.
+
+## Regressions and progressions
+
+These tables list tests which encountered a regression or progression during the
+specified time period, which is currently set to the last 21 days.
+
+## Trendline Graphs
+
+Trendline graphs show measured per run averages of MRR values, NDR or PDR
+values, group average values, and detected anomalies.
+The graphs are constructed as follows:
+
+- X-axis represents the date in the format MMDD.
+- Y-axis represents run-average MRR value, NDR or PDR values in Mpps. For PDR
+ tests also a graph with average latency at 50% PDR [us] is generated.
+- Markers to indicate anomaly classification:
+
+ - Regression - red circle.
+ - Progression - green circle.
+
+- The line shows average MRR value of each group.
+
+In addition the graphs show dynamic labels while hovering over graph data
+points, presenting the CSIT build date, measured value, VPP reference, trend job
+build ID and the LF testbed ID.
--- /dev/null
+---
+bookToc: false
+title: "TRex Traffic Generator"
+weight: 5
+---
+
+# TRex Traffic Generator
+
+## Usage
+
+[TRex traffic generator](https://trex-tgn.cisco.com) is used for majority of
+CSIT performance tests. TRex is used in multiple types of performance tests,
+see [Data Plane Throughtput]({{< ref "data_plane_throughput/data_plane_throughput/#Data Plane Throughtput" >}})
+for more detail.
+
+## Traffic modes
+
+TRex is primarily used in two (mutually incompatible) modes.
+
+### Stateless mode
+
+Sometimes abbreviated as STL.
+A mode with high performance, which is unable to react to incoming traffic.
+We use this mode whenever it is possible.
+Typical test where this mode is not applicable is NAT44ED,
+as DUT does not assign deterministic outside address+port combinations,
+so we are unable to create traffic that does not lose packets
+in out2in direction.
+
+Measurement results are based on simple L2 counters
+(opackets, ipackets) for each traffic direction.
+
+### Stateful mode
+
+A mode capable of reacting to incoming traffic.
+Contrary to the stateless mode, only UDP and TCP is supported
+(carried over IPv4 or IPv6 packets).
+Performance is limited, as TRex needs to do more CPU processing.
+TRex suports two subtypes of stateful traffic,
+CSIT uses ASTF (Advanced STateFul mode).
+
+This mode is suitable for NAT44ED tests, as clients send packets from inside,
+and servers react to it, so they see the outside address and port to respond to.
+Also, they do not send traffic before NAT44ED has created the corresponding
+translation entry.
+
+When possible, L2 counters (opackets, ipackets) are used.
+Some tests need L7 counters, which track protocol state (e.g. TCP),
+but those values are less than reliable on high loads.
+
+## Traffic Continuity
+
+Generated traffic is either continuous, or limited (by number of transactions).
+Both modes support both continuities in principle.
+
+### Continuous traffic
+
+Traffic is started without any data size goal.
+Traffic is ended based on time duration, as hinted by search algorithm.
+This is useful when DUT behavior does not depend on the traffic duration.
+The default for stateless mode.
+
+### Limited traffic
+
+Traffic has defined data size goal (given as number of transactions),
+duration is computed based on this goal.
+Traffic is ended when the size goal is reached,
+or when the computed duration is reached.
+This is useful when DUT behavior depends on traffic size,
+e.g. target number of NAT translation entries, each to be hit exactly once
+per direction.
+This is used mainly for stateful mode.
+
+## Traffic synchronicity
+
+Traffic can be generated synchronously (test waits for duration)
+or asynchronously (test operates during traffic and stops traffic explicitly).
+
+### Synchronous traffic
+
+Trial measurement is driven by given (or precomputed) duration,
+no activity from test driver during the traffic.
+Used for most trials.
+
+### Asynchronous traffic
+
+Traffic is started, but then the test driver is free to perform
+other actions, before stopping the traffic explicitly.
+This is used mainly by reconf tests, but also by some trials
+used for runtime telemetry.
+
+## Trafic profiles
+
+TRex supports several ways to define the traffic.
+CSIT uses small Python modules based on Scapy as definitions.
+Details of traffic profiles depend on modes (STL or ASTF),
+but some are common for both modes.
+
+Search algorithms are intentionally unaware of the traffic mode used,
+so CSIT defines some terms to use instead of mode-specific TRex terms.
+
+### Transactions
+
+TRex traffic profile defines a small number of behaviors,
+in CSIT called transaction templates. Traffic profiles also instruct
+TRex how to create a large number of transactions based on the templates.
+
+Continuous traffic loops over the generated transactions.
+Limited traffic usually executes each transaction once
+(typically as constant number of loops over source addresses,
+each loop with different source ports).
+
+Currently, ASTF profiles define one transaction template each.
+Number of packets expected per one transaction varies based on profile details,
+as does the criterion for when a transaction is considered successful.
+
+Stateless transactions are just one packet (sent from one TG port,
+successful if received on the other TG port).
+Thus unidirectional stateless profiles define one transaction template,
+bidirectional stateless profiles define two transaction templates.
+
+### TPS multiplier
+
+TRex aims to open transaction specified by the profile at a steady rate.
+While TRex allows the transaction template to define its intended "cps" value,
+CSIT does not specify it, so the default value of 1 is applied,
+meaning TRex will open one transaction per second (and transaction template)
+by default. But CSIT invocation uses "multiplier" (mult) argument
+when starting the traffic, that multiplies the cps value,
+meaning it acts as TPS (transactions per second) input.
+
+With a slight abuse of nomenclature, bidirectional stateless tests
+set "packets per transaction" value to 2, just to keep the TPS semantics
+as a unidirectional input value.
+
+### Duration stretching
+
+TRex can be IO-bound, CPU-bound, or have any other reason
+why it is not able to generate the traffic at the requested TPS.
+Some conditions are detected, leading to TRex failure,
+for example when the bandwidth does not fit into the line capacity.
+But many reasons are not detected.
+
+Unfortunately, TRex frequently reacts by not honoring the duration
+in synchronous mode, taking longer to send the traffic,
+leading to lower then requested load offered to DUT.
+This usualy breaks assumptions used in search algorithms,
+so it has to be avoided.
+
+For stateless traffic, the behavior is quite deterministic,
+so the workaround is to apply a fictional TPS limit (max_rate)
+to search algorithms, usually depending only on the NIC used.
+
+For stateful traffic the behavior is not deterministic enough,
+for example the limit for TCP traffic depends on DUT packet loss.
+In CSIT we decided to use logic similar to asynchronous traffic.
+The traffic driver sleeps for a time, then stops the traffic explicitly.
+The library that parses counters into measurement results
+than usually treats unsent packets/transactions as lost/failed.
+
+We have added a IP4base tests for every NAT44ED test,
+so that users can compare results.
+If the results are very similar, it is probable TRex was the bottleneck.
+
+### Startup delay
+
+By investigating TRex behavior, it was found that TRex does not start
+the traffic in ASTF mode immediately. There is a delay of zero traffic,
+after which the traffic rate ramps up to the defined TPS value.
+
+It is possible to poll for counters during the traffic
+(fist nonzero means traffic has started),
+but that was found to influence the NDR results.
+
+Thus "sleep and stop" stategy is used, which needs a correction
+to the computed duration so traffic is stopped after the intended
+duration of real traffic. Luckily, it turns out this correction
+is not dependend on traffic profile nor CPU used by TRex,
+so a fixed constant (0.112 seconds) works well.
+Unfortunately, the constant may depend on TRex version,
+or execution environment (e.g. TRex in AWS).
+
+The result computations need a precise enough duration of the real traffic,
+luckily server side of TRex has precise enough counter for that.
+
+It is unknown whether stateless traffic profiles also exhibit a startup delay.
+Unfortunately, stateless mode does not have similarly precise duration counter,
+so some results (mostly MRR) are affected by less precise duration measurement
+in Python part of CSIT code.
+
+## Measuring Latency
+
+If measurement of latency is requested, two more packet streams are
+created (one for each direction) with TRex flow_stats parameter set to
+STLFlowLatencyStats. In that case, returned statistics will also include
+min/avg/max latency values and encoded HDRHistogram data.
\ No newline at end of file
--- /dev/null
+---
+bookToc: false
+title: "Tunnel Encapsulations"
+weight: 10
+---
+
+# Tunnel Encapsulations
+
+Tunnel encapsulations testing is grouped based on the type of outer
+header: IPv4 or IPv6.
+
+## IPv4 Tunnels
+
+VPP is tested in the following IPv4 tunnel baseline configurations:
+
+- *ip4vxlan-l2bdbase*: VXLAN over IPv4 tunnels with L2 bridge-domain MAC
+ switching.
+- *ip4vxlan-l2xcbase*: VXLAN over IPv4 tunnels with L2 cross-connect.
+- *ip4lispip4-ip4base*: LISP over IPv4 tunnels with IPv4 routing.
+- *ip4lispip6-ip6base*: LISP over IPv4 tunnels with IPv6 routing.
+- *ip4gtpusw-ip4base*: GTPU over IPv4 tunnels with IPv4 routing.
+
+In all cases listed above low number of MAC, IPv4, IPv6 flows (253 or 254 per
+direction) is switched or routed by VPP.
+
+In addition selected IPv4 tunnels are tested at scale:
+
+- *dot1q--ip4vxlanscale-l2bd*: VXLAN over IPv4 tunnels with L2 bridge-
+ domain MAC switching, with scaled up dot1q VLANs (10, 100, 1k),
+ mapped to scaled up L2 bridge-domains (10, 100, 1k), that are in turn
+ mapped to (10, 100, 1k) VXLAN tunnels. 64.5k flows are transmitted per
+ direction.
+
+## IPv6 Tunnels
+
+VPP is tested in the following IPv6 tunnel baseline configurations:
+
+- *ip6lispip4-ip4base*: LISP over IPv4 tunnels with IPv4 routing.
+- *ip6lispip6-ip6base*: LISP over IPv4 tunnels with IPv6 routing.
+
+In all cases listed above low number of IPv4, IPv6 flows (253 or 254 per
+direction) is routed by VPP.
--- /dev/null
+---
+bookToc: false
+title: "VPP_Device Functional"
+weight: 18
+---
+
+# VPP_Device Functional
+
+Includes VPP_Device test environment for functional VPP
+device tests integrated into LFN CI/CD infrastructure. VPP_Device tests
+run on 1-Node testbeds (1n-skx, 1n-arm) and rely on Linux SRIOV Virtual
+Function (VF), dot1q VLAN tagging and external loopback cables to
+facilitate packet passing over external physical links. Initial focus is
+on few baseline tests. New device tests can be added by small edits
+to existing CSIT Performance (2-node) test. RF test definition code
+stays unchanged with the exception of traffic generator related L2 KWs.
--- /dev/null
+---
+bookToc: false
+title: "VPP Forwarding Modes"
+weight: 3
+---
+
+# VPP Forwarding Modes
+
+VPP is tested in a number of L2, IPv4 and IPv6 packet lookup and
+forwarding modes. Within each mode baseline and scale tests are
+executed, the latter with varying number of FIB entries.
+
+## L2 Ethernet Switching
+
+VPP is tested in three L2 forwarding modes:
+
+- *l2patch*: L2 patch, the fastest point-to-point L2 path that loops
+ packets between two interfaces without any Ethernet frame checks or
+ lookups.
+- *l2xc*: L2 cross-connect, point-to-point L2 path with all Ethernet
+ frame checks, but no MAC learning and no MAC lookup.
+- *l2bd*: L2 bridge-domain, multipoint-to-multipoint L2 path with all
+ Ethernet frame checks, with MAC learning (unless static MACs are used)
+ and MAC lookup.
+
+l2bd tests are executed in baseline and scale configurations:
+
+- *l2bdbase*: Two MAC FIB entries are learned by VPP to enable packet
+ switching between two interfaces in two directions. VPP L2 switching
+ is tested with 254 IPv4 unique flows per direction, varying IPv4
+ source address per flow in order to invoke RSS based packet
+ distribution across VPP workers. The same source and destination MAC
+ address is used for all flows per direction. IPv4 source address is
+ incremented for every packet.
+
+- *l2bdscale*: A high number of MAC FIB entries are learned by VPP to
+ enable packet switching between two interfaces in two directions.
+ Tested MAC FIB sizes include: i) 10k with 5k unique flows per
+ direction, ii) 100k with 2 x 50k flows and iii) 1M with 2 x 500k
+ flows. Unique flows are created by using distinct source and
+ destination MAC addresses that are changed for every packet using
+ incremental ordering, making VPP learn (or refresh) distinct src MAC
+ entries and look up distinct dst MAC entries for every packet. For
+ details, see
+ [Packet Flow Ordering]({{< ref "packet_flow_ordering#Packet Flow Ordering" >}}).
+
+Ethernet wire encapsulations tested include: untagged, dot1q, dot1ad.
+
+## IPv4 Routing
+
+IPv4 routing tests are executed in baseline and scale configurations:
+
+- *ip4base*: Two /32 IPv4 FIB entries are configured in VPP to enable
+ packet routing between two interfaces in two directions. VPP routing
+ is tested with 253 IPv4 unique flows per direction, varying IPv4
+ source address per flow in order to invoke RSS based packet
+ distribution across VPP workers. IPv4 source address is incremented
+ for every packet.
+
+- *ip4scale*: A high number of /32 IPv4 FIB entries are configured in
+ VPP. Tested IPv4 FIB sizes include: i) 20k with 10k unique flows per
+ direction, ii) 200k with 2 * 100k flows and iii) 2M with 2 * 1M
+ flows. Unique flows are created by using distinct IPv4 destination
+ addresses that are changed for every packet, using incremental or
+ random ordering. For details, see
+ [Packet Flow Ordering]({{< ref "packet_flow_ordering#Packet Flow Ordering" >}}).
+
+## IPv6 Routing
+
+Similarly to IPv4, IPv6 routing tests are executed in baseline and scale
+configurations:
+
+- *ip6base*: Two /128 IPv4 FIB entries are configured in VPP to enable
+ packet routing between two interfaces in two directions. VPP routing
+ is tested with 253 IPv6 unique flows per direction, varying IPv6
+ source address per flow in order to invoke RSS based packet
+ distribution across VPP workers. IPv6 source address is incremented
+ for every packet.
+
+- *ip4scale*: A high number of /128 IPv6 FIB entries are configured in
+ VPP. Tested IPv6 FIB sizes include: i) 20k with 10k unique flows per
+ direction, ii) 200k with 2 * 100k flows and iii) 2M with 2 * 1M
+ flows. Unique flows are created by using distinct IPv6 destination
+ addresses that are changed for every packet, using incremental or
+ random ordering. For details, see
+ [Packet Flow Ordering]({{< ref "packet_flow_ordering#Packet Flow Ordering" >}}).
+
+## SRv6 Routing
+
+SRv6 routing tests are executed in a number of baseline configurations,
+in each case SR policy and steering policy are configured for one
+direction and one (or two) SR behaviours (functions) in the other
+directions:
+
+- *srv6enc1sid*: One SID (no SRH present), one SR function - End.
+- *srv6enc2sids*: Two SIDs (SRH present), two SR functions - End and
+ End.DX6.
+- *srv6enc2sids-nodecaps*: Two SIDs (SRH present) without decapsulation,
+ one SR function - End.
+- *srv6proxy-dyn*: Dynamic SRv6 proxy, one SR function - End.AD.
+- *srv6proxy-masq*: Masquerading SRv6 proxy, one SR function - End.AM.
+- *srv6proxy-stat*: Static SRv6 proxy, one SR function - End.AS.
+
+In all listed cases low number of IPv6 flows (253 per direction) is
+routed by VPP.
--- /dev/null
+---
+bookToc: false
+title: "VPP Startup Settings"
+weight: 17
+---
+
+# VPP Startup Settings
+
+CSIT code manipulates a number of VPP settings in startup.conf for
+optimized performance. List of common settings applied to all tests and
+test dependent settings follows.
+
+## Common Settings
+
+List of VPP startup.conf settings applied to all tests:
+
+1. heap-size <value> - set separately for ip4, ip6, stats, main
+ depending on scale tested.
+2. no-tx-checksum-offload - disables UDP / TCP TX checksum offload in
+ DPDK. Typically needed for use faster vector PMDs (together with
+ no-multi-seg).
+3. buffers-per-numa <value> - sets a number of memory buffers allocated
+ to VPP per CPU socket. VPP default is 16384. Needs to be increased for
+ scenarios with large number of interfaces and worker threads. To
+ accommodate for scale tests, CSIT is setting it to the maximum possible
+ value corresponding to the limit of DPDK memory mappings (currently
+ 256). For Xeon Skylake platforms configured with 2MB hugepages and VPP
+ data-size and buffer-size defaults (2048B and 2496B respectively), this
+ results in value of 215040 (256 * 840 = 215040, 840 * 2496B buffers fit
+ in 2MB hugepage).
+
+## Per Test Settings
+
+List of vpp startup.conf settings applied dynamically per test:
+
+1. corelist-workers <list_of_cores> - list of logical cores to run VPP
+ worker data plane threads. Depends on HyperThreading and core per
+ test configuration.
+2. num-rx-queues <value> - depends on a number of VPP threads and NIC
+ interfaces.
+3. no-multi-seg - disables multi-segment buffers in DPDK, improves
+ packet throughput, but disables Jumbo MTU support. Disabled for all
+ tests apart from the ones that require Jumbo 9000B frame support.
+4. UIO driver - depends on topology file definition.
+5. QAT VFs - depends on NRThreads, each thread = 1QAT VFs.
--- /dev/null
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Code Review / csit.git / commitdiff
