chore(mtu): delete some unused keywords related to mtu

[csit.git] / resources / libraries / python / PLRsearch / PLRsearch.py

diff --git a/resources/libraries/python/PLRsearch/PLRsearch.py b/resources/libraries/python/PLRsearch/PLRsearch.py
index cdfd308..0e78cc9 100644 (file)
--- a/resources/libraries/python/PLRsearch/PLRsearch.py
+++ b/resources/libraries/python/PLRsearch/PLRsearch.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021 Cisco and/or its affiliates.
+# Copyright (c) 2022 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:
 # 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:


@@ -426,13 +426,21 @@ class PLRsearch:
         Integrator assumes uniform distribution, but over different parameters.
         Weight and likelihood are used interchangeably here anyway.
 
         Integrator assumes uniform distribution, but over different parameters.
         Weight and likelihood are used interchangeably here anyway.
 

-        Each trial has an offered load, a duration and a loss count.
-        Fitting function is used to compute the average loss per second.
-        Poisson distribution (with average loss per trial) is used
+        Each trial has an intended load, a sent count and a loss count
+        (probably counting unsent packets as loss, as they signal
+        the load is too high for the traffic generator).
+        The fitting function is used to compute the average loss rate.
+        Geometric distribution (with average loss per trial) is used

         to get likelihood of one trial result, the overal likelihood
         is a product of all trial likelihoods.
         As likelihoods can be extremely small, logarithms are tracked instead.
 
         to get likelihood of one trial result, the overal likelihood
         is a product of all trial likelihoods.
         As likelihoods can be extremely small, logarithms are tracked instead.
 

+        The current implementation does not use direct loss rate
+        from the fitting function, as the input and output units may not match
+        (e.g. intended load in TCP transactions, loss in packets).
+        Instead, the expected average loss is scaled according to the number
+        of packets actually sent.
+

         TODO: Copy ReceiveRateMeasurement from MLRsearch.
 
         :param trace: A multiprocessing-friendly logging function (closure).
         TODO: Copy ReceiveRateMeasurement from MLRsearch.
 
         :param trace: A multiprocessing-friendly logging function (closure).


@@ -556,6 +564,20 @@ class PLRsearch:
             :rtype: multiprocessing.Connection
             """
 
             :rtype: multiprocessing.Connection
             """
 

+            boss_pipe_end, worker_pipe_end = multiprocessing.Pipe()
+            # Starting the worker first. Contrary to documentation
+            # https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection
+            # sending of large object without active listener on the other side
+            # results in a deadlock, not in a ValueError.
+            # See https://stackoverflow.com/questions/15137292/large-objects-and-multiprocessing-pipes-and-send
+            worker = multiprocessing.Process(
+                target=Integrator.try_estimate_nd,
+                args=(worker_pipe_end, 10.0, self.trace_enabled)
+            )
+            worker.daemon = True
+            worker.start()
+
+            # Only now it is safe to send the function to compute with.

             def value_logweight_func(trace, x_mrr, x_spread):
                 """Return log of critical rate and log of likelihood.
 
             def value_logweight_func(trace, x_mrr, x_spread):
                 """Return log of critical rate and log of likelihood.
 


@@ -601,15 +623,6 @@ class PLRsearch:
                 return value, logweight
 
             dilled_function = dill.dumps(value_logweight_func)
                 return value, logweight
 
             dilled_function = dill.dumps(value_logweight_func)

-            boss_pipe_end, worker_pipe_end = multiprocessing.Pipe()
-            # Do not send yet, run the worker first to avoid a deadlock.
-            # See https://stackoverflow.com/a/15716500
-            worker = multiprocessing.Process(
-                target=Integrator.try_estimate_nd,
-                args=(worker_pipe_end, 10.0, self.trace_enabled)
-            )
-            worker.daemon = True
-            worker.start()

             boss_pipe_end.send(
                 (dimension, dilled_function, focus_tracker, max_samples)
             )
             boss_pipe_end.send(
                 (dimension, dilled_function, focus_tracker, max_samples)
             )