1 Multiple Loss Ratio Search library
2 ==================================
7 This library was developed as a speedup for traditional binary search
8 in CSIT_ (Continuous System and Integration Testing) project of fd.io_
9 (Fast Data), one of LFN_ (Linux Foundation Networking) projects.
11 In order to make this code available in PyPI_ (Python Package Index),
12 the setuputils stuff (later converted to pyproject.toml) has been added,
13 but after some discussion, that directory_ ended up having
14 only a symlink to the original place of tightly coupled CSIT code.
19 The currently published `IETF draft`_ describes the logic of version 1.2.0,
20 earlier library and draft versions do not match each other that well.
25 High level description
26 ______________________
28 A complete application capable of testing performance using MLRsearch
29 consists of three layers: Manager, Controller and Measurer.
30 This library provides an implementation for the Controller only,
31 including all the classes needed to define API between Controller
32 and other two components.
34 Users are supposed to implement the whole Manager layer,
35 and also implement the Measurer layer.
36 The Measurer instance is injected as a parameter
37 when the manager calls the controller instance.
39 The purpose of Measurer instance is to perform one trial measurement.
40 Upon invocation of measure() method, the controller only specifies
41 the intended duration and the intended load for the trial.
42 The call is done using keyword arguments, so the signature has to be:
44 .. code-block:: python3
46 def measure(self, intended_duration, intended_load):
48 Usually, the trial measurement process also needs other values,
49 collectively caller a traffic profile. User (the manager instance)
50 is responsible for initiating the measurer instance accordingly.
51 Also, the manager is supposed to set up SUT, traffic generator,
52 and any other component that can affect the result.
54 For specific input and output objects see the example below.
59 This is a minimal example showing every configuration attribute.
60 The measurer does not interact with any real SUT,
61 it simulates a SUT that is able to forward exactly one million packets
62 per second (unidirectional traffic only),
63 not one packet more (fully deterministic).
64 In these conditions, the conditional throughput for PDR
65 happens to be accurate within one packet per second.
67 This is the screen capture of interactive python interpreter
68 (wrapped so long lines are readable):
70 .. code-block:: python3
72 >>> import dataclasses
73 >>> from MLRsearch import (
74 ... AbstractMeasurer, Config, MeasurementResult,
75 ... MultipleLossRatioSearch, SearchGoal,
78 >>> class Hard1MppsMeasurer(AbstractMeasurer):
79 ... def measure(self, intended_duration, intended_load):
80 ... sent = int(intended_duration * intended_load)
81 ... received = min(sent, int(intended_duration * 1e6))
82 ... return MeasurementResult(
83 ... intended_duration=intended_duration,
84 ... intended_load=intended_load,
85 ... offered_count=sent,
86 ... forwarding_count=received,
90 ... print(".", end="")
92 >>> ndr_goal = SearchGoal(
94 ... exceed_ratio=0.005,
95 ... relative_width=0.005,
96 ... initial_trial_duration=1.0,
97 ... final_trial_duration=1.0,
98 ... duration_sum=21.0,
99 ... preceding_targets=2,
100 ... expansion_coefficient=2,
102 >>> pdr_goal = dataclasses.replace(ndr_goal, loss_ratio=0.005)
104 ... goals=[ndr_goal, pdr_goal],
107 ... search_duration_max=1.0,
108 ... warmup_duration=None,
110 >>> controller = MultipleLossRatioSearch(config=config)
111 >>> result = controller.search(measurer=Hard1MppsMeasurer(), debug=print_dot)
112 ....................................................................................
113 ....................................................................................
114 ...................>>> print(result)
115 {SearchGoal(loss_ratio=0.0, exceed_ratio=0.005, relative_width=0.005, initial_trial_
116 duration=1.0, final_trial_duration=1.0, duration_sum=21.0, preceding_targets=2, expa
117 nsion_coefficient=2, fail_fast=True): fl=997497.6029392382,s=(gl=21.0,bl=0.0,gs=0.0,
118 bs=0.0), SearchGoal(loss_ratio=0.005, exceed_ratio=0.005, relative_width=0.005, init
119 ial_trial_duration=1.0, final_trial_duration=1.0, duration_sum=21.0, preceding_targe
120 ts=2, expansion_coefficient=2, fail_fast=True): fl=1002508.6747611101,s=(gl=21.0,bl=
122 >>> print(f"NDR conditional throughput: {float(result[ndr_goal].conditional_throughp
124 NDR conditional throughput: 997497.6029392382
125 >>> print(f"PDR conditional throughput: {float(result[pdr_goal].conditional_throughp
127 PDR conditional throughput: 1000000.6730730429
133 1.2.1: Updated the readme document.
135 1.2.0: Changed the output structure to use Goal Result as described in draft-05.
137 1.1.0: Logic improvements, independent selectors, exceed ratio support,
138 better width rounding, conditional throughput as output.
139 Implementation relies more on dataclasses, code split into smaller files.
140 API changed considerably, mainly to avoid long argument lists.
142 0.4.0: Considarable logic improvements, more than two target ratios supported.
143 API is not backward compatible with previous versions.
145 0.3.0: Migrated to Python 3.6, small code quality improvements.
147 0.2.0: Optional parameter "doublings" has been added.
149 0.1.1: First officially released version.
151 .. _CSIT: https://wiki.fd.io/view/CSIT
152 .. _fd.io: https://fd.io/
153 .. _LFN: https://www.linuxfoundation.org/projects/networking/
154 .. _PyPI: https://pypi.org/project/MLRsearch/
155 .. _directory: https://gerrit.fd.io/r/gitweb?p=csit.git;a=tree;f=PyPI/MLRsearch
156 .. _IETF draft: https://tools.ietf.org/html/draft-ietf-bmwg-mlrsearch-05