C-Docs: New structure

Change-Id: I73d107f94b28b138f3350a9e1eedb0555583a9ca
Signed-off-by: Tibor Frank <tifrank@cisco.com>

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----
-title: "MLRsearch"
-weight: 2
----
-
-# MLRsearch
-
-## 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[^1] 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-as a warm-up) are taken into consideration, unless superseded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`[^2].
-
-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).
-
-[^1]: [binary search](https://en.wikipedia.org/wiki/Binary_search)
-[^2]: [exponential search](https://en.wikipedia.org/wiki/Exponential_search)