Network Measurements @ Planète
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Transcript of Network Measurements @ Planète
Network Measurements @ Planète
Chadi BarakatEmail: [email protected]://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]