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| -rw-r--r-- | docs/ietf/draft-vpolak-bmwg-plrsearch-02.md (renamed from docs/ietf/draft-vpolak-bmwg-plrsearch-01.md) | 2 |
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diff --git a/docs/ietf/draft-vpolak-bmwg-plrsearch-00.md b/docs/ietf/draft-vpolak-bmwg-plrsearch-00.md deleted file mode 100644 index 6d52fb1c1e..0000000000 --- a/docs/ietf/draft-vpolak-bmwg-plrsearch-00.md +++ /dev/null @@ -1,228 +0,0 @@ ---- -title: Probabilistic Loss Ratio Search for Packet Throughput (PLRsearch) -# abbrev: PLRsearch -docname: draft-vpolak-bmwg-plrsearch-00 -date: 2018-11-13 - -ipr: trust200902 -area: ops -wg: Benchmarking Working Group -kw: Internet-Draft -cat: info - -coding: us-ascii -pi: # can use array (if all yes) or hash here -# - toc -# - sortrefs -# - symrefs - toc: yes - sortrefs: # defaults to yes - symrefs: yes - -author: - - - ins: M. Konstantynowicz - name: Maciek Konstantynowicz - org: Cisco Systems - role: editor - email: mkonstan@cisco.com - - - ins: V. Polak - name: Vratko Polak - org: Cisco Systems - role: editor - email: vrpolak@cisco.com - -normative: - RFC2544: - RFC8174: - -informative: - ---- abstract - -This document addresses challenges while applying methodologies -described in [RFC2544] to benchmarking NFV (Network Function -Virtualization) over an extended period of time, sometimes referred to -as "soak testing". More specifically to benchmarking software based -implementations of NFV data planes. Packet throughput search approach -proposed by this document assumes that system under test is -probabilistic in nature, and not deterministic. - ---- middle - -# Motivation - -Network providers are interested in throughput a device can sustain. - -RFC 2544 assumes loss ratio is given by a deterministic function of -offered load. But NFV software devices are not deterministic (enough). -This leads for deterministic algorithms (such as MLRsearch with single -trial) to return results, which when repeated show relatively high -standard deviation, thus making it harder to tell what "the throughput" -actually is. - -We need another algorithm, which takes this indeterminism into account. - -# Model - -Each algorithm searches for an answer to a precisely formulated -question. When the question involves indeterministic systems, it has to -specify probabilities (or prior distributions) which are tied to a -specific probabilistic model. Different models will have different -number (and meaning) of parameters. Complicated (but more realistic) -models have many parameters, and the math involved can be very -complicated. It is better to start with simpler probabilistic model, and -only change it when the output of the simpler algorithm is not stable or -useful enough. - -TODO: Refer to packet forwarding terminology, such as "offered load" and -"loss ratio". - -TODO: Mention that no packet duplication is expected (or is filtered -out). - -TODO: Define critical load and critical region earlier. - -This document is focused on algorithms related to packet loss count -only. No latency (or other information) is taken into account. For -simplicity, only one type of measurement is considered: dynamically -computed offered load, constant within trial measurement of -predetermined trial duration. - -Also, running longer trials (in some situations) could be more efficient, -but in order to perform trial at multiple offered loads withing critical region, -trial durations should be kept as short as possible. - -# Poisson Distribution - -TODO: Give link to more officially published literature about Poisson -distribution. - -Note-1: that the algorithm makes an assumption that packet traffic -generator detects duplicate packets on receive detection, and reports -this as an error. - -Note-2: Binomial distribution is a better fit compared to Poisson -distribution (acknowledging that the number of packets lost cannot be -higher than the number of packets offered), but the difference tends to -be relevant in loads far above the critical region, so using Poisson -distribution helps the algorithm focus on critical region better. - -When comparing different offered loads, the average loss per second is -assumed to increase, but the (deterministic) function from offered load -into average loss rate is otherwise unknown. - -Given a loss target (configurable, by default one packet lost per -second), there is an unknown offered load when the average is exactly -that. We call that the "critical load". If critical load seems higher -than maximum offerable load, we should use the maximum offerable load to -make search output more stable. - -Of course, there are great many increasing functions. The offered load -has to be chosen for each trial, and the computed posterior distribution -of critical load can change with each trial result. - -To make the space of possible functions more tractable, some other -simplifying assumption is needed. As the algorithm will be examining -(also) loads close to the critical load, linear approximation to the -function (TODO: name the function) in the critical region is important. -But as the search algorithm needs to evaluate the function also far -away from the critical region, the approximate function has to be well- -behaved for every positive offered load, specifically it cannot predict -non-positive packet loss rate. - -Within this document, "fitting function" is the name for such well-behaved -function which approximates the unknown function in the critical region. - -Results from trials far from the critical region are likely to affect -the critical rate estimate negatively, as the fitting function does not -need to be a good approximation there. Instead of discarding some -results, or "suppressing" their impact with ad-hoc methods (other than -using Poisson distribution instead of binomial) is not used, as such -methods tend to make the overall search unstable. We rely on most of -measurements being done (eventually) within the critical region, and -overweighting far-off measurements (eventually) for well-behaved fitting -functions. - -# Fitting Function Coefficients Distribution - -To accomodate systems with different behaviours, the fitting function is -expected to have few numeric parameters affecting its shape (mainly -affecting the linear approximation in the critical region). - -The general search algorithm can use whatever increasing fitting -function, some specific functions can be described later. - -TODO: Describe sigmoid-based and erf-based functions. - -It is up to implementer to chose a fitting function and prior -distribution of its parameters. The rest of this document assumes each -parameter is independently and uniformly distributed over common -interval. Implementers are to add non-linear transformations into their -fitting functions if their prior is different. - -TODO: Move the following sentence into more appropriate place. - -Speaking about new trials, each next trial will be done at offered load -equal to the current average of the critical load. - -Exit condition is either critical load stdev becoming small enough, or -overal search time becoming long enough. - -The algorithm should report both avg and stdev for critical load. If the -reported averages follow a trend (without reaching equilibrium), avg and -stdev should refer to the equilibrium estibated based on the trend, not -to immediate posterior values. - -TODO: Explicitly mention the iterative character of the search. - -# Algorithm Formulas - -## Integration - -The posterior distributions for fitting function parameters will not be -integrable in general. - -The search algorithm utilises the fact that trial measurement takes some -time, so this time can be used for numeric integration (using suitable -method, such as Monte Carlo) to achieve sufficient precision. - -## Optimizations - -After enough trials, the posterior distribution will be concentrated in -a narrow area of parameter space. The integration method could take -advantage of that. - -Even in the concentrated area, the likelihood can be quite small, so the -integration algorithm should track the logarithm of the likelihood, and -also avoid underflow errors bu ther means. - -# Known Implementations - -The only known working implementatin of Probabilistic Loss Ratio Search -for Packet Throughput is in Linux Foundation FD.io CSIT project. https://wiki.fd.io/view/CSIT. https://git.fd.io/csit/. - -## FD.io CSIT Implementation Specifics - -In a sample implemenation in FD.io CSIT project, there is around 0.5 -second delay between trials due to restrictons imposed by packet traffic -generator in use (T-Rex), avoiding that delay is out of scope of this -document. - -TODO: Describe how the current integration algorithm finds the -concentrated area. - -# IANA Considerations - -.. - -# Security Considerations - -.. - -# Acknowledgements - -.. - ---- back
\ No newline at end of file diff --git a/docs/ietf/draft-vpolak-bmwg-plrsearch-01.md b/docs/ietf/draft-vpolak-bmwg-plrsearch-02.md index 57e1a61125..dc03135753 100644 --- a/docs/ietf/draft-vpolak-bmwg-plrsearch-01.md +++ b/docs/ietf/draft-vpolak-bmwg-plrsearch-02.md @@ -2,7 +2,7 @@ title: Probabilistic Loss Ratio Search for Packet Throughput (PLRsearch) # abbrev: PLRsearch docname: draft-vpolak-bmwg-plrsearch-01 -date: 2019-03-11 +date: 2019-xx-xx ipr: trust200902 area: ops |