Benchmarking Working Group M. Konstantynowicz, Ed.
Internet-Draft V. Polak, Ed.
Intended status: Informational Cisco Systems
Expires: April 25, 2019 October 22, 2018
Probabilistic Loss Ratio Search for Packet Throughput (PLRsearch)
draft-vpolak-plrsearch-00
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.
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This Internet-Draft will expire on April 25, 2019.
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Table of Contents
1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3. Poisson Distribution . . . . . . . . . . . . . . . . . . . . 3
4. Fitting Function Coefficients Distribution . . . . . . . . . 4
5. Algorithm Formulas . . . . . . . . . . . . . . . . . . . . . 5
5.1. Integration . . . . . . . . . . . . . . . . . . . . . . . 5
5.2. Optimizations . . . . . . . . . . . . . . . . . . . . . . 5
6. Known Implementations . . . . . . . . . . . . . . . . . . . . 5
6.1. FD.io CSIT Implementation Specifics . . . . . . . . . . . 5
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 5
8. Security Considerations . . . . . . . . . . . . . . . . . . . 6
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 6
10. Normative References . . . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 6
1. 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.
2. 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".
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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.
3. 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
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(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.
4. 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),
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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.
5. Algorithm Formulas
5.1. 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.
5.2. 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.
6. 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/.
6.1. 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.
7. IANA Considerations
..
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8. Security Considerations
..
9. Acknowledgements
..
10. Normative References
[RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for
Network Interconnect Devices", RFC 2544,
DOI 10.17487/RFC2544, March 1999,
.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, .
Authors' Addresses
Maciek Konstantynowicz (editor)
Cisco Systems
Email: mkonstan@cisco.com
Vratko Polak (editor)
Cisco Systems
Email: vrpolak@cisco.com
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