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Edge-based Network Modeling and Inference Vinay Ribeiro, Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu.

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Presentation on theme: "Edge-based Network Modeling and Inference Vinay Ribeiro, Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu."— Presentation transcript:

1 Edge-based Network Modeling and Inference Vinay Ribeiro, Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu

2 Rice University – spin.rice.edu 2 INCITE Project

3 Rice University – spin.rice.edu 3 Available Bandwidth Estimation Available bandwidth = unused bandwidth on path Key metric for data-intensive applications Estimate ABW by e2e active probing

4 Rice University – spin.rice.edu 4 pathChirp Tool Based on principle of self-induced congestion Exponentially spaced chirp probe trains

5 Rice University – spin.rice.edu 5 Internet Experiments 3 common hops between SLAC Rice and Chicago Rice paths Estimates fall in proportion to introduced Poisson traffic

6 Rice University – spin.rice.edu 6 pathChirp – Summary Balances probing uncertainty principle Efficient –performs comparably to state-of-the-art tools (PathLoad, PacketPair, TOPP) using about 10x fewer packets Robust to bursty traffic –incorporates multiscale statistical analysis Open-source software available at spin.rice.edu See poster Tuesday night

7 Rice University – spin.rice.edu 7 Alpha+Beta Model Causes of burstiness in network traffic (non-Gaussianity) ? Mean 99% = + alpha beta

8 Rice University – spin.rice.edu 8 Alpha+Beta Model Causes of burstiness in network traffic (non-Gaussianity) ? Mean 99% = + alpha beta

9 Rice University – spin.rice.edu 9 Traffic Bursts: A Case Study Typical non-spiky epoch Load of each connection in the time bin: Considerable balanced field of connections 10 KB

10 Rice University – spin.rice.edu 10 Load of each connection offered in the time bin: One connection dominates! 150 KB 15 KB Traffic Bursts: A Case Study Typical spiky epoch Typical non-spiky epoch Load of each connection in the time bin: Considerable balanced field of connections 10 KB

11 Rice University – spin.rice.edu 11 Beta Alpha fractional Gaussian noisestable Levy noise + = + + = Bottlenecked elsewhere Large RTT Bottlenecked at this point Large file + small RTT

12 Rice University – spin.rice.edu 12 spin.rice.edu dsp.rice.edu

13 Rice University – spin.rice.edu 13 CAIDA Gigabit Testbed Smartbit cross-traffic generator Estimates track changes in available bandwidth Performance improves with increasing packet size

14 Rice University – spin.rice.edu 14 Grid Computing Harness global resources to improve performance

15 Rice University – spin.rice.edu 15 Application: Predict Download Time Dynamically schedule tasks based on bandwidth availability

16 Rice University – spin.rice.edu 16 Optimal Path Selection Choose path to minimize download time from A to D

17 Rice University – spin.rice.edu 17 Active Probing for Bandwidth Iperf, Pathload, TOPP, … Self-induced congestion principle: increase probing rate until queuing delay increases Goal: Minimally intrusive Lightweight probing with as few packets as possible

18 Rice University – spin.rice.edu 18 Chirp Probing Chirp: exponential flight pattern of probes Non-intrusive and Efficient: wide range of probing bit rates, few packets

19 Rice University – spin.rice.edu 19 Comparison with Pathload Rice ECE network 100Mbps links pathChirp can use 10x fewer bytes for comparable accuracy Available bandwidth EfficiencyAccuracy pathchirppathloadpathChirp 10-90% pathload Avg.min-max 30Mbps0.35MB3.9MB19-29Mbps16-31Mbps 50Mbps0.75MB5.6MB39-48Mbps39-52Mbps 70Mbps0.6MB8.6MB54-63Mbps63-74Mbps

20 Rice University – spin.rice.edu 20 Conclusions pathChirp: non-intrusive available bandwidth probing tool Successful tests on the Internet and Gigabit testbed Upto 10x improvement over state-of-the-art pathload on Rice ECE network Whats next?


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