feat(MLRsearch): MLRsearch v7

Replaces MLRv2, suitable for "big bang" upgrade across CSIT.
PyPI metadata updated only partially (full edits will come separately).
Pylint wants less complexity, but the differences are only minor.

+ Use the same (new CSIT) defaults everywhere, also in Python library.
+ Update also PLRsearch to use the new result class.
+ Make upper bound optional in UTI.
+ Fix ASTF approximate duration detection.
+ Do not keep approximated_receive_rate (for MRR) in result structure.

Change-Id: I03406f32d5c93f56b527cb3f93791b61955dfd74
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 14 insertions, 14 deletions

diff --git a/resources/libraries/python/PLRsearch/PLRsearch.py b/resources/libraries/python/PLRsearch/PLRsearch.py
index 0314a80efb..7599a9e64d 100644
--- a/resources/libraries/python/PLRsearch/PLRsearch.py
+++ b/resources/libraries/python/PLRsearch/PLRsearch.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022 Cisco and/or its affiliates.
+# Copyright (c) 2023 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:
@@ -195,8 +195,8 @@ class PLRsearch:
             # exponential impact. Make it configurable, or is 4:3 good enough?
             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:
+                    lossy_loads.append(measurement.intended_load)
+            if measurement.loss_ratio > 0.0:
                 zeros = 0
             lossy_loads.sort()
             if stop_time <= time.time():
@@ -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.relative_receive_rate / (
+                next_load = (measurement.relative_forwarding_rate / (
                     1.0 - self.packet_loss_ratio_target))
             else:
                 next_load = (avg1 + avg2) / 2.0
@@ -441,7 +441,7 @@ class PLRsearch:
         Instead, the expected average loss is scaled according to the number
         of packets actually sent.
 
-        TODO: Copy ReceiveRateMeasurement from MLRsearch.
+        TODO: Copy MeasurementResult from MLRsearch.
 
         :param trace: A multiprocessing-friendly logging function (closure).
         :param lfit_func: Fitting function, typically lfit_spread or lfit_erf.
@@ -450,7 +450,7 @@ class PLRsearch:
         :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
+        :type trial_result_list: list of MLRsearch.MeasurementResult
         :type mrr: float
         :type spread: float
         :returns: Logarithm of result weight for given function and parameters.
@@ -460,17 +460,17 @@ class PLRsearch:
         trace(u"log_weight for mrr", mrr)
         trace(u"spread", spread)
         for result in trial_result_list:
-            trace(u"for tr", result.target_tr)
+            trace(u"for tr", result.intended_load)
             trace(u"lc", result.loss_count)
-            trace(u"d", result.duration)
-            # _rel_ values use units of target_tr (transactions per second).
+            trace(u"d", result.intended_duration)
+            # _rel_ values use units of intended_load (transactions per second).
             log_avg_rel_loss_per_second = lfit_func(
-                trace, result.target_tr, mrr, spread
+                trace, result.intended_load, mrr, spread
             )
             # _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
+                result.offered_count / result.intended_load
             )
             # Geometric probability computation for logarithms.
             log_trial_likelihood = log_plus(0.0, -log_avg_abs_loss_per_trial)
@@ -524,7 +524,7 @@ class PLRsearch:
         :param max_samples: Limit for integrator samples, for debugging.
         :type trial_duration: float
         :type transmit_rate: float
-        :type trial_result_list: list of MLRsearch.ReceiveRateMeasurement
+        :type trial_result_list: list of MLRsearch.MeasurementResult
         :type min_rate: float
         :type max_rate: float
         :type focus_trackers: 2-tuple of None or stat_trackers.VectorStatTracker
@@ -705,7 +705,7 @@ class PLRsearch:
         :param measurement: The trial measurement obtained during computation.
         :param stretch_result: Computation output for stretch fitting function.
         :param erf_result: Computation output for erf fitting function.
-        :type measurement: ReceiveRateMeasurement
+        :type measurement: MeasurementResult
         :type stretch_result: _PartialResult
         :type erf_result: _PartialResult
         :returns: Combined results.
@@ -754,7 +754,7 @@ _ComputeResult = namedtuple(
 :param stretch_exp_avg: Stretch fitting function estimate average exponentiated.
 :param erf_exp_avg: Erf fitting function estimate average, exponentiated.
 :param trackers: Pair of focus trackers to start next iteration with.
-:type measurement: ReceiveRateMeasurement
+:type measurement: MeasurementResult
 :type avg: float
 :type stdev: float
 :type stretch_exp_avg: float