from abc import ABCMeta, abstractmethod
from enum import Enum, unique
+
@unique
class SearchDirection(Enum):
"""Direction of linear search."""
TOP_DOWN = 1
BOTTOM_UP = 2
+
@unique
class SearchResults(Enum):
"""Result of the drop rate search."""
FAILURE = 2
SUSPICIOUS = 3
+
@unique
class RateType(Enum):
"""Type of rate units."""
PACKETS_PER_SECOND = 2
BITS_PER_SECOND = 3
+
@unique
class LossAcceptanceType(Enum):
"""Type of the loss acceptance criteria."""
FRAMES = 1
PERCENTAGE = 2
+
@unique
class SearchResultType(Enum):
"""Type of search result evaluation."""
BEST_OF_N = 1
WORST_OF_N = 2
+
class DropRateSearch(object):
"""Abstract class with search algorithm implementation."""
__metaclass__ = ABCMeta
def __init__(self):
- #duration of traffic run (binary, linear)
+ # duration of traffic run (binary, linear)
self._duration = 60
- #initial start rate (binary, linear)
+ # initial start rate (binary, linear)
self._rate_start = 100
- #step of the linear search, unit: RateType (self._rate_type)
+ # step of the linear search, unit: RateType (self._rate_type)
self._rate_linear_step = 10
- #last rate of the binary search, unit: RateType (self._rate_type)
+ # last rate of the binary search, unit: RateType (self._rate_type)
self._last_binary_rate = 0
- #linear search direction, permitted values: SearchDirection
+ # linear search direction, permitted values: SearchDirection
self._search_linear_direction = SearchDirection.TOP_DOWN
- #upper limit of search, unit: RateType (self._rate_type)
+ # upper limit of search, unit: RateType (self._rate_type)
self._rate_max = 100
- #lower limit of search, unit: RateType (self._rate_type)
+ # lower limit of search, unit: RateType (self._rate_type)
self._rate_min = 1
- #permitted values: RateType
+ # permitted values: RateType
self._rate_type = RateType.PERCENTAGE
- #accepted loss during search, units: LossAcceptanceType
+ # accepted loss during search, units: LossAcceptanceType
self._loss_acceptance = 0
- #permitted values: LossAcceptanceType
+ # permitted values: LossAcceptanceType
self._loss_acceptance_type = LossAcceptanceType.FRAMES
- #size of frames to send
+ # size of frames to send
self._frame_size = "64"
- #binary convergence criterium type is self._rate_type
- self._binary_convergence_threshold = 100000
- #numbers of traffic runs during one rate step
+ # binary convergence criterium type is self._rate_type
+ self._binary_convergence_threshold = 5000
+ # numbers of traffic runs during one rate step
self._max_attempts = 1
- #type of search result evaluation, unit: SearchResultType
+ # type of search result evaluation, unit: SearchResultType
self._search_result_type = SearchResultType.BEST_OF_N
- #result of search
+ # result of search
self._search_result = None
self._search_result_rate = None
loss_acceptance_type, traffic_type):
"""Send traffic from TG and measure count of dropped frames.
- :param rate: offered traffic load
- :param frame_size: size of frame
- :param loss_acceptance: permitted drop ratio or frames count
- :param loss_acceptance_type: type of permitted loss
- :param traffic_type: traffic profile ([2,3]-node-L[2,3], ...)
+ :param rate: Offered traffic load.
+ :param frame_size: Size of frame.
+ :param loss_acceptance: Permitted drop ratio or frames count.
+ :param loss_acceptance_type: Type of permitted loss.
+ :param traffic_type: Traffic profile ([2,3]-node-L[2,3], ...).
:type rate: int
:type frame_size: str
:type loss_acceptance: float
:type loss_acceptance_type: LossAcceptanceType
:type traffic_type: str
- :return: drop threshold exceeded? (True/False)
+ :return: Drop threshold exceeded? (True/False)
:rtype bool
"""
pass
def set_search_rate_boundaries(self, max_rate, min_rate):
"""Set search boundaries: min,max.
- :param max_rate: upper value of search boundaries
- :param min_rate: lower value of search boundaries
+ :param max_rate: Upper value of search boundaries.
+ :param min_rate: Lower value of search boundaries.
:type max_rate: float
:type min_rate: float
:return: nothing
def set_search_linear_step(self, step_rate):
"""Set step size for linear search.
- :param step_rate: linear search step size
+ :param step_rate: Linear search step size.
:type step_rate: float
:return: nothing
"""
def _set_search_rate_type(self, rate_type):
"""Set rate type to one of RateType-s.
- :param rate_type: type of rate to set
+ :param rate_type: Type of rate to set.
:type rate_type: RateType
:return: nothing
"""
def set_search_frame_size(self, frame_size):
"""Set size of frames to send.
- :param frame_size: size of frames
+ :param frame_size: Size of frames.
:type frame_size: str
:return: nothing
"""
def set_duration(self, duration):
"""Set the duration of single traffic run.
- :param duration: number of seconds for traffic to run
+ :param duration: Number of seconds for traffic to run.
:type duration: int
:return: nothing
"""
def get_duration(self):
"""Return configured duration of single traffic run.
- :return: number of seconds for traffic to run
+ :return: Number of seconds for traffic to run.
:rtype: int
"""
return self._duration
def set_binary_convergence_threshold(self, convergence):
"""Set convergence for binary search.
- :param convergence: treshold value number
+ :param convergence: Treshold value number.
:type convergence: float
:return: nothing
"""
def get_binary_convergence_threshold(self):
"""Get convergence for binary search.
- :return: treshold value number
+ :return: Treshold value number.
:rtype: float
"""
return self._binary_convergence_threshold
def get_rate_type_str(self):
"""Return rate type representation.
- :return: string representation of rate type
+ :return: String representation of rate type.
:rtype: str
"""
if self._rate_type == RateType.PERCENTAGE:
def set_max_attempts(self, max_attempts):
"""Set maximum number of traffic runs during one rate step.
- :param max_attempts: number of traffic runs
+ :param max_attempts: Number of traffic runs.
:type max_attempts: int
:return: nothing
"""
def get_max_attempts(self):
"""Return maximum number of traffic runs during one rate step.
- :return: number of traffic runs
+ :return: Number of traffic runs.
:rtype: int
"""
return self._max_attempts
def _set_search_result_type(self, search_type):
"""Set type of search result evaluation to one of SearchResultType.
- :param search_type: type of search result evaluation to set
+ :param search_type: Type of search result evaluation to set.
:type search_type: SearchResultType
:return: nothing
"""
else:
self._search_result_type = search_type
- def _get_best_of_n(self, res_list):
+ @staticmethod
+ def _get_best_of_n(res_list):
"""Return best result of N traffic runs.
- :param res_list: list of return values from all runs at one rate step
+ :param res_list: List of return values from all runs at one rate step.
:type res_list: list
- :return: True if at least one run is True, False otherwise
+ :return: True if at least one run is True, False otherwise.
:rtype: boolean
"""
- #Return True if any element of the iterable is True.
+ # Return True if any element of the iterable is True.
return any(res_list)
- def _get_worst_of_n(self, res_list):
+ @staticmethod
+ def _get_worst_of_n(res_list):
"""Return worst result of N traffic runs.
- :param res_list: list of return values from all runs at one rate step
+ :param res_list: List of return values from all runs at one rate step.
:type res_list: list
- :return: False if at least one run is False, True otherwise
+ :return: False if at least one run is False, True otherwise.
:rtype: boolean
"""
- #Return False if not all elements of the iterable are True.
+ # Return False if not all elements of the iterable are True.
return not all(res_list)
- def _get_result_based_on_search_type(self, res_list):
+ def _get_res_based_on_search_type(self, res_list):
"""Return result of search based on search evaluation type.
- :param res_list: list of return values from all runs at one rate step
+ :param res_list: List of return values from all runs at one rate step.
:type res_list: list
- :return: Boolean based on search result type
+ :return: Boolean based on search result type.
:rtype: boolean
"""
if self._search_result_type == SearchResultType.BEST_OF_N:
else:
raise ValueError("Unknown search result type")
-
def linear_search(self, start_rate, traffic_type):
"""Linear search of rate with loss below acceptance criteria.
- :param start_rate: initial rate
- :param traffic_type: traffic profile
+ :param start_rate: Initial rate.
+ :param traffic_type: Traffic profile.
:type start_rate: float
- :param traffic_type: str
+ :type traffic_type: str
:return: nothing
"""
raise ValueError("Start rate is not in min,max range")
rate = float(start_rate)
- #the last but one step
+ # the last but one step
prev_rate = None
- #linear search
+ # linear search
while True:
res = []
- for n in range(self._max_attempts):
+ for dummy in range(self._max_attempts):
res.append(self.measure_loss(rate, self._frame_size,
self._loss_acceptance,
self._loss_acceptance_type,
traffic_type))
- res = self._get_result_based_on_search_type(res)
+ res = self._get_res_based_on_search_type(res)
if self._search_linear_direction == SearchDirection.BOTTOM_UP:
- #loss occured and it was above acceptance criteria
- if res == False:
- #if this is first run then we didn't find drop rate
- if prev_rate == None:
+ # loss occurred and it was above acceptance criteria
+ if not res:
+ # if this is first run then we didn't find drop rate
+ if prev_rate is None:
self._search_result = SearchResults.FAILURE
self._search_result_rate = None
return
self._search_result = SearchResults.SUCCESS
self._search_result_rate = prev_rate
return
- #there was no loss / loss below acceptance criteria
- elif res == True:
+ # there was no loss / loss below acceptance criteria
+ elif res:
prev_rate = rate
rate += self._rate_linear_step
if rate > self._rate_max:
if prev_rate != self._rate_max:
- #one last step with rate set to _rate_max
+ # one last step with rate set to _rate_max
rate = self._rate_max
continue
else:
raise RuntimeError("Unknown search result")
elif self._search_linear_direction == SearchDirection.TOP_DOWN:
- #loss occured, decrease rate
- if res == False:
+ # loss occurred, decrease rate
+ if not res:
prev_rate = rate
rate -= self._rate_linear_step
if rate < self._rate_min:
if prev_rate != self._rate_min:
- #one last step with rate set to _rate_min
+ # one last step with rate set to _rate_min
rate = self._rate_min
continue
else:
return
else:
continue
- #no loss => non/partial drop rate found
- elif res == True:
+ # no loss => non/partial drop rate found
+ elif res:
self._search_result = SearchResults.SUCCESS
self._search_result_rate = rate
return
def verify_search_result(self):
"""Fail if search was not successful.
- :return: result rate
+ :return: Result rate.
:rtype: float
"""
if self._search_result == SearchResults.FAILURE:
raise Exception('Search FAILED')
- elif self._search_result in [SearchResults.SUCCESS, SearchResults.SUSPICIOUS]:
+ elif self._search_result in [SearchResults.SUCCESS,
+ SearchResults.SUSPICIOUS]:
return self._search_result_rate
- def binary_search(self, b_min, b_max, traffic_type):
+ def binary_search(self, b_min, b_max, traffic_type, skip_max_rate=False):
"""Binary search of rate with loss below acceptance criteria.
- :param b_min: min range rate
- :param b_max: max range rate
- :param traffic_type: traffic profile
+ :param b_min: Min range rate.
+ :param b_max: Max range rate.
+ :param traffic_type: Traffic profile.
+ :param skip_max_rate: Start with max rate first
:type b_min: float
:type b_max: float
:type traffic_type: str
+ :type skip_max_rate: bool
:return: nothing
"""
if not self._rate_min <= float(b_max) <= self._rate_max:
raise ValueError("Max rate is not in min,max range")
if float(b_max) < float(b_min):
- raise ValueError("Min rate is greater then max rate")
+ raise ValueError("Min rate is greater than max rate")
- #binary search
- #rate is half of interval + start of interval
- rate = ((float(b_max) - float(b_min)) / 2) + float(b_min)
- #rate diff with previous run
+ # binary search
+ if skip_max_rate:
+ # rate is half of interval + start of interval
+ rate = ((float(b_max) - float(b_min)) / 2) + float(b_min)
+ else:
+ # rate is max of interval
+ rate = float(b_max)
+ # rate diff with previous run
rate_diff = abs(self._last_binary_rate - rate)
- #convergence criterium
+ # convergence criterium
if float(rate_diff) < float(self._binary_convergence_threshold):
if not self._search_result_rate:
self._search_result = SearchResults.FAILURE
self._last_binary_rate = rate
res = []
- for n in range(self._max_attempts):
+ for dummy in range(self._max_attempts):
res.append(self.measure_loss(rate, self._frame_size,
self._loss_acceptance,
self._loss_acceptance_type,
traffic_type))
- res = self._get_result_based_on_search_type(res)
+ res = self._get_res_based_on_search_type(res)
- #loss occured and it was above acceptance criteria
- if res == False:
- self.binary_search(b_min, rate, traffic_type)
- #there was no loss / loss below acceptance criteria
- elif res == True:
+ # loss occurred and it was above acceptance criteria
+ if not res:
+ self.binary_search(b_min, rate, traffic_type, True)
+ # there was no loss / loss below acceptance criteria
+ else:
self._search_result_rate = rate
- self.binary_search(rate, b_max, traffic_type)
+ self.binary_search(rate, b_max, traffic_type, True)
+
+ def combined_search(self, start_rate, traffic_type):
+ """Combined search of rate with loss below acceptance criteria.
+
+ :param start_rate: Initial rate.
+ :param traffic_type: Traffic profile.
+ :type start_rate: float
+ :type traffic_type: str
+ :return: nothing
+ """
+
+ self.linear_search(start_rate, traffic_type)
+
+ if self._search_result in [SearchResults.SUCCESS,
+ SearchResults.SUSPICIOUS]:
+ b_min = self._search_result_rate
+ b_max = self._search_result_rate + self._rate_linear_step
+
+ # we found max rate by linear search
+ if self.floats_are_close_equal(float(b_min), self._rate_max):
+ return
+
+ # limiting binary range max value into max range
+ if float(b_max) > self._rate_max:
+ b_max = self._rate_max
+
+ # reset result rate
+ temp_rate = self._search_result_rate
+ self._search_result_rate = None
+
+ # we will use binary search to refine search in one linear step
+ self.binary_search(b_min, b_max, traffic_type, True)
+
+ # linear and binary search succeed
+ if self._search_result == SearchResults.SUCCESS:
+ return
+ # linear search succeed but binary failed or suspicious
+ else:
+ self._search_result = SearchResults.SUSPICIOUS
+ self._search_result_rate = temp_rate
else:
- raise RuntimeError("Unknown search result")
+ raise RuntimeError("Linear search FAILED")
+
+ @staticmethod
+ def floats_are_close_equal(num_a, num_b, rel_tol=1e-9, abs_tol=0.0):
+ """Compares two float numbers for close equality.
+
+ :param num_a: First number to compare.
+ :param num_b: Second number to compare.
+ :param rel_tol=1e-9: The relative tolerance.
+ :param abs_tol=0.0: The minimum absolute tolerance level.
+ :type num_a: float
+ :type num_b: float
+ :type rel_tol: float
+ :type abs_tol: float
+ :return: Returns True if num_a is close in value to num_b or equal.
+ False otherwise.
+ :rtype: boolean
+ """
+
+ if num_a == num_b:
+ return True
+
+ if rel_tol < 0.0 or abs_tol < 0.0:
+ raise ValueError('Error tolerances must be non-negative')
+
+ return abs(num_b - num_a) <= max(rel_tol * max(abs(num_a), abs(num_b)),
+ abs_tol)
- def combined_search(self):
- raise NotImplementedError
Code Review / csit.git / blobdiff