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diff --git a/docs/report/introduction/methodology_data_plane_throughput/methodology_mlrsearch_tests.rst b/docs/report/introduction/methodology_data_plane_throughput/methodology_mlrsearch_tests.rst deleted file mode 100644 index 1f0da11f49..0000000000 --- a/docs/report/introduction/methodology_data_plane_throughput/methodology_mlrsearch_tests.rst +++ /dev/null @@ -1,88 +0,0 @@ -.. _mlrsearch_algorithm: - -MLRsearch Tests -^^^^^^^^^^^^^^^ - -Overview -~~~~~~~~ - -Multiple Loss Ratio search (MLRsearch) tests use an optimized search algorithm -implemented in FD.io CSIT project. MLRsearch discovers any number of -loss ratio loads in a single search. - -Two loss ratio goals are of interest in FD.io CSIT, leading to Non-Drop Rate -(NDR, loss ratio goal is exact zero) and Partial Drop Rate -(PDR, non-zero loss ratio goal, currently 0.5%). - -MLRsearch discovers all the loads in a single pass, reducing required time -duration compared to separate `binary search`_es for each rate. Overall -search time is reduced even further by relying on shorter trial -durations of intermediate steps, with only the final measurements -conducted at the specified final trial duration. This results in the -shorter overall execution time when compared to standard NDR/PDR binary -search, while guaranteeing similar results. - -.. Note:: All throughput rates are *always* bi-directional - aggregates of two equal (symmetric) uni-directional packet rates - received and reported by an external traffic generator, - unless the test specifically requires unidirectional traffic. - -Search Implementation -~~~~~~~~~~~~~~~~~~~~~ - -Detailed description of the MLRsearch algorithm is included in the IETF -draft `draft-ietf-bmwg-mlrsearch-02 -<https://datatracker.ietf.org/doc/html/draft-ietf-bmwg-mlrsearch-02>`_ -that is in the process of being standardized in the IETF Benchmarking -Methodology Working Group (BMWG). -(Newer version is published in IETF, describing improvements not yet used -in CSIT production.) - -MLRsearch is also available as a `PyPI (Python Package Index) library -<https://pypi.org/project/MLRsearch/>`_. - -Algorithm highlights -~~~~~~~~~~~~~~~~~~~~ - -MRR and receive rate at MRR load are used as initial guesses for the search. - -All previously measured trials (except the very first one which can act -as a warm-up) are taken into consideration, unless superseded -by a trial at the same load but higher duration. - -For every loss ratio goal, tightest upper and lower bound -(from results of large enough trial duration) form an interval. -Exit condition is given by that interval reaching low enough relative width. -Small enough width is achieved by bisecting the current interval. -The bisection can be uneven, to save measurements based on information theory. - -Switching to higher trial duration generally requires a re-measure -at a load from previous trial duration. -When the re-measurement does not confirm previous bound classification -(e.g. tightest lower bound at shorter trial duration becomes -a newest tightest upper bound upon re-measurement), -external search is used to find close enough bound of the lost type. -External search is a generalization of the first stage of `exponential search`_. - -Shorter trial durations use double width goal, -because one bisection is always safe before risking external search. - -Within an iteration for a specific trial duration, smaller loss ratios (NDR) -are narrowed down first before search continues with higher loss ratios (PDR). - -Other heuristics are there, aimed to prevent unneccessarily narrow intervals, -and to handle corner cases around min and max load. - -Deviations from RFC 2544 -~~~~~~~~~~~~~~~~~~~~~~~~ - -CSIT does not have any explicit wait times before and after trial traffic. - -Small differences between intended and offered load are tolerated, -mainly due to various time overheads preventing precise measurement -of the traffic duration (and TRex can sometimes suffer from duration stretching). - -The final trial duration is only 30s (10s for reconf tests). - -.. _binary search: https://en.wikipedia.org/wiki/Binary_search -.. _exponential search: https://en.wikipedia.org/wiki/Exponential_search |