Edge-based Network Modeling and Inference

1. Edge-basedNetwork Modelingand Inference Vinay Ribeiro, Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu

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

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

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

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

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

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

11. Beta Alpha • Bottlenecked at this point • Large file + small RTT • Bottlenecked elsewhere • Large RTT + + + = = fractionalGaussian noise stableLevy noise Rice University – spin.rice.edu

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

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

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

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

19. Comparison with Pathload • Rice ECE network • 100Mbps links • pathChirp can use 10x fewer bytes for comparable accuracy 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 • What’s next? Rice University – spin.rice.edu