X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Flibraries%2Fpython%2FPLRsearch%2FPLRsearch.py;h=cdfd3081494ac9c246162e60d6011029bcb2165f;hb=9780b57a9640e9ab40e40ec122ac80e09cd74c79;hp=ec58fbd10f61c9136c01539e229960daec9a729e;hpb=3f0822a35efc2fd61bcc4040bea13519f389b655;p=csit.git

diff --git a/resources/libraries/python/PLRsearch/PLRsearch.py b/resources/libraries/python/PLRsearch/PLRsearch.py
index ec58fbd10f..cdfd308149 100644
--- a/resources/libraries/python/PLRsearch/PLRsearch.py
+++ b/resources/libraries/python/PLRsearch/PLRsearch.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020 Cisco and/or its affiliates.
+# Copyright (c) 2021 Cisco and/or its affiliates.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at:
@@ -54,7 +54,7 @@ class PLRsearch:
 
     def __init__(
             self, measurer, trial_duration_per_trial, packet_loss_ratio_target,
-            trial_number_offset=0, timeout=1800.0, trace_enabled=False):
+            trial_number_offset=0, timeout=7200.0, trace_enabled=False):
         """Store rate measurer and additional parameters.
 
         The measurer must never report negative loss count.
@@ -193,7 +193,7 @@ class PLRsearch:
             zeros += 1
             # TODO: Ratio of fill rate to drain rate seems to have
             # exponential impact. Make it configurable, or is 4:3 good enough?
-            if measurement.loss_fraction >= self.packet_loss_ratio_target:
+            if measurement.loss_ratio >= self.packet_loss_ratio_target:
                 for _ in range(4 * zeros):
                     lossy_loads.append(measurement.target_tr)
             if measurement.loss_count > 0:
@@ -205,7 +205,7 @@ class PLRsearch:
             if (trial_number - self.trial_number_offset) <= 1:
                 next_load = max_rate
             elif (trial_number - self.trial_number_offset) <= 3:
-                next_load = (measurement.receive_rate / (
+                next_load = (measurement.relative_receive_rate / (
                     1.0 - self.packet_loss_ratio_target))
             else:
                 next_load = (avg1 + avg2) / 2.0
@@ -439,7 +439,7 @@ class PLRsearch:
         :param lfit_func: Fitting function, typically lfit_spread or lfit_erf.
         :param trial_result_list: List of trial measurement results.
         :param mrr: The mrr parameter for the fitting function.
-        :param spread: The spread parameter for the fittinmg function.
+        :param spread: The spread parameter for the fitting function.
         :type trace: function (str, object) -> None
         :type lfit_func: Function from 3 floats to float.
         :type trial_result_list: list of MLRsearch.ReceiveRateMeasurement
@@ -455,20 +455,21 @@ class PLRsearch:
             trace(u"for tr", result.target_tr)
             trace(u"lc", result.loss_count)
             trace(u"d", result.duration)
-            log_avg_loss_per_second = lfit_func(
+            # _rel_ values use units of target_tr (transactions per second).
+            log_avg_rel_loss_per_second = lfit_func(
                 trace, result.target_tr, mrr, spread
             )
-            log_avg_loss_per_trial = (
-                log_avg_loss_per_second + math.log(result.duration)
+            # _abs_ values use units of loss count (maybe packets).
+            # There can be multiple packets per transaction.
+            log_avg_abs_loss_per_trial = log_avg_rel_loss_per_second + math.log(
+                result.transmit_count / result.target_tr
             )
-            # Poisson probability computation works nice for logarithms.
-            log_trial_likelihood = (
-                result.loss_count * log_avg_loss_per_trial
-                - math.exp(log_avg_loss_per_trial)
-            )
-            log_trial_likelihood -= math.lgamma(1 + result.loss_count)
+            # Geometric probability computation for logarithms.
+            log_trial_likelihood = log_plus(0.0, -log_avg_abs_loss_per_trial)
+            log_trial_likelihood *= -result.loss_count
+            log_trial_likelihood -= log_plus(0.0, +log_avg_abs_loss_per_trial)
             log_likelihood += log_trial_likelihood
-            trace(u"avg_loss_per_trial", math.exp(log_avg_loss_per_trial))
+            trace(u"avg_loss_per_trial", math.exp(log_avg_abs_loss_per_trial))
             trace(u"log_trial_likelihood", log_trial_likelihood)
         return log_likelihood