Network Measurements @ Planète

Chadi BarakatEmail: Chadi.Barakat@inria.frhttp://planete.inria.fr/chadi/

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Covered topics

Traffic measurements in the core-Packet sampling [Infocom,IMC,ITC,Presto@CoNext]

Edge measurements of Internet access performance-Delay monitoring [ITC,GIS@Infocom]

Applications’ traffic measurements-Application identification [Infocom,Networking,ICC]-Video streaming [CoNext]

Particular focus on the scalability of measurements and the limitation of their overhead

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1- Traffic measurements in the core

Common configuration-NetFlow at edge-Packet sampling-Static rates

Simple but,-reduced coverage-lacks adaptability and flexibility

Our approach (funded by FP7 ECODE led by Alcatel-Lucent):-Sample traffic over the network and combine measurements-Optimize/Adapt sampling rates given a measurement task

E.g. maximum accuracy for NetFlow records, traffic matrix of some ASes/prefixes

1- Problem formulation

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Network-wide measurements-Combine the different local and noisy measurements to build a global and more

reliable estimation of traffic

Sampling rate optimization-Find the sampling rate vector that minimizes a weighted sum of mean square

estimation error over tasks

Two implemented solutions: (netflow/s)

- Either one shot (requires overhead prediction)- Otherwise iterative using Gradients

1- MonLab: A platform for the validation of network trafic monitoring solutions

http://planete.inria.fr/MonLab/

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Emulate network topologies (routers, routing)

Replay real traffic traces

Implement real monitoring tools

(tcpdump, Sampling, SoftFlow)

Available in Open Source

Implement our algorithms

In collaboration with Grenouille.com (funded by ANR CMON)

Context – Large scale measurements of network access:-Bandwidth, delay, anomalies, neutrality, etc-Problem of scale and lack of collaboration of operators-(Volunteered) Users do the maximum, their measurements correlated,

with the help of dedicated servers

First project: ACQUA – a scanner of my access delay-Is there a network problem? How many paths are impacted?-Ratio of impacted paths points to gravity (and locality)-Track network delay to random landmarks (sample access tree)-Few landmarks are enough – iPlane data [ITC09]

ACQUA for service differentiation

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2- Edge measurements of access performance Internet weather

Average delay

Average delay

over abnormal paths

Ratio of abnormal

path delays

http://planete.inria.fr/acqua/

Second project: Can one use coordinates for network monitoring instead of direct delay measurements?

Virtual coordinates:-General purpose service for delay estimation and host positioning-By embedding partial network delays in an Euclidean space-Available information in P2P applications (Vivaldi @ Azurus)

Observations [GIS@Infocom2010]:-Vivaldi coordinates move even in normal situations (PlanetLab)-But there is a cluster of stable nodes that move together-Network can be monitored by tracking content of this cluster,

the downside is a slow reaction time

2- Edge measurements of access performance

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3- Applications’ traffic measurements

Objectives:-Understand and model traffic of major applications-Use the resulted models for application identification

-Without solely relying on port numbers and payload

Profiling, dimensioning, anomalies, etc

Example of two contributions:-A statistical iterative method for application identification using packet level (size,

time) and host level (profile) measurements-A study of video streaming traffic for different players

Activities will extend to further applications/protocols (VoIP, P2P, etc)

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3- Iterative Bayesian approach for application identification on the fly

Start from a trace where reality of applications is known Build a histogram for the features of each packet of each application

-E.g. size of packet 1, time of packet 1, size of packet 2, etc

On the fly•Capture a packet, get its feature•Get the corresponding probability per application•Update a global likelihood function per application

•Stop when either a threshold or a maximum number of iterations are reached

•Map the flow to the most likely application

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Rat

io o

f cor

rect

ly c

lass

ified

flow

s

Packet number / application

3- Iterative Bayesian approach for application identification on the fly

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3- Characterizing video streaming traffic

Motivated by the increase in streaming traffic (20% to 40%)

Understand its fingerprint on the network for different players

Data:-Youtube: 5000 FLASH, 3000 HTML5, 2000 HD FLASH, 50 mobile

-Netflix: 200 to Desktop, 50 to mobile

Three main strategies identified

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3- Characterizing video streaming traffic

No On Off

Cycles

Long On Off Cycles

OFF OFF

Short On Off Cycles

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3- Characterizing video streaming traffic

Motivated by the increase in streaming traffic (20% to 40%)

Understand its fingerprint on the network for different players

Data:-Youtube: 5000 FLASH, 3000 HTML5, 2000 HD FLASH, 50 mobile

-Netflix: 200 to Desktop, 50 to mobile

Three main strategies identified

An analytical model to capture the impact of the different

strategies on the aggregate network traffic:-No impact if videos are not interrupted-Otherwise, waste of resources for greedy strategies

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Concluding remarks

Everything is scaling up, measurements should follow-Sampling, inversion, compression-More monitors (passive/active). Correlating measurements.-Need for dedicated infrastructure

-Capture, probe, reply to probes, perform computations, store data, etc

Applications behave far from standards-Measurements and models are needed

Access performance for the large public-More faithful (“my measurements”)-Easier to understand (application level metrics?)

Real traces are a big issue. Experimental platforms another one.

merci

www.inria.fr

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Context

Scalable solutions for network and traffic measurements-Improve accuracy while limiting the overhead

Understand the performance of existing solutions-NetFlow, coordinates, localization, etc

Propose new solutions-Traffic classification, access delay, etc

Observe and understand the network behavior-Traffic, applications, protocols, etc

1- Adaptive network-wide sampling

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Traffic inference block

Sampling rate configuration block

Sampled flow monitoring deployed in all routers

Monitoring application e.g, calculate user traffic, estimate flow sizes, track traffic as function of time

Optimize some accuracy functionwhile maintaining sampling rates and overhead below some threshold

Iterate to adapt to netw

ork conditions

1- Case study: Traffic matrix calculation

Estimate amount of traffic flowing among a set of edge routers(common task for traffic engineering apps)

GEANT European Research Network

MonLab (planete.inria.fr/monlab/): An experimental platform that integrates:Sampled NetFlow + Collector + Online optimizer of the sampling rates + Traffic emulator + Overhead measurement

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1- Sample of results: Precision vs Target Overhead

When the sampling rates are optimally set for the edge solution

Small flows are better captured by our method

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[Infocom 2011, ITC 2011, Presto@CoNEXT 2010]