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**Edge-based Network Modeling and Inference**

Came out of my personal experience with 301 – fourier analysis and linear systems Vinay Ribeiro, Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu

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**Rice University – spin.rice.edu**

INCITE Project Rice University – spin.rice.edu

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**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

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**Rice University – spin.rice.edu**

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

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**Rice University – spin.rice.edu**

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

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**Rice University – spin.rice.edu**

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

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**Rice University – spin.rice.edu**

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

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**Rice University – spin.rice.edu**

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

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**Traffic Bursts: A Case Study**

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

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**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 Load of each connection offered in the time bin: One connection dominates! 150 KB 15 KB Rice University – spin.rice.edu

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**Rice University – spin.rice.edu**

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

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**spin.rice.edu dsp.rice.edu**

Rice University – spin.rice.edu

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**Rice University – spin.rice.edu**

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

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**Rice University – spin.rice.edu**

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

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**Application: Predict Download Time**

Dynamically schedule tasks based on bandwidth availability Rice University – spin.rice.edu

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**Optimal Path Selection**

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

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**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

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**Rice University – spin.rice.edu**

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

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**Comparison with Pathload**

Rice ECE network 100Mbps links pathChirp can use 10x fewer bytes for comparable accuracy Available bandwidth Efficiency Accuracy pathchirp pathload pathChirp 10-90% Avg.min-max 30Mbps 0.35MB 3.9MB 19-29Mbps 16-31Mbps 50Mbps 0.75MB 5.6MB 39-48Mbps 39-52Mbps 70Mbps 0.6MB 8.6MB 54-63Mbps 63-74Mbps Rice University – spin.rice.edu

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**Rice University – spin.rice.edu**

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

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