1. 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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INCITE Project
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3. Available Bandwidth Estimation
Available bandwidth = unused bandwidth on path
Key metric for data-intensive applications
Estimate ABW by e2e active probing
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pathChirp Tool
Based on principle of self-induced congestion
Exponentially spaced chirp probe trains
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5. Rice University – spin.rice.edu
Internet Experiments
3 common hops between SLACRice and ChicagoRice paths
Estimates fall in proportion to introduced Poisson traffic
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6. 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

7. 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

8. 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

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
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10. 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
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11. 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
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12. spin.rice.edu dsp.rice.edu
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CAIDA Gigabit Testbed
Smartbit cross-traffic generator
Estimates track changes in available bandwidth
Performance improves with increasing packet size
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Grid Computing
Harness global resources to improve performance
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15. Application: Predict Download Time
Dynamically schedule tasks based on bandwidth availability
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16. Optimal Path Selection
Choose path to minimize download time from A to D
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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
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18. 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

19. 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
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20. 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