Edge-based Network Modeling and Inference Vinay Ribeiro, Rolf Riedi, Richard Baraniuk Rice University spin.rice.edu.
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Edge-basedNetwork Modeling
and Inference
Vinay Ribeiro, Rolf Riedi, Richard Baraniuk
Rice University
spin.rice.edu
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 spaced chirp probe trains
Rice University – spin.rice.edu 5
Internet Experiments
• 3 common hops between SLACRice and ChicagoRice 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
• Causes of burstiness in network traffic(non-Gaussianity)?
Mean
99%= +
alpha beta
Rice University – spin.rice.edu 8
Alpha+Beta Model
• Causes of burstiness in network traffic(non-Gaussianity)?
Mean
99%= +
alpha beta
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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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
Rice University – spin.rice.edu 11
Beta Alpha
fractional Gaussian noise stable Levy noise
+
=
+
+
=
0 2000 4000 60000
0.5
1
1.5
2
x 105
time (1 unit=500ms)
num
ber
of b
ytes
0 2000 4000 60000
0.5
1
1.5
2
2.5
3x 10
5
time (1 unit=500ms)nu
mbe
r of
byt
es
• Bottlenecked elsewhere
• Large RTT
• Bottlenecked at this point
• Large file + small RTT
Rice University – spin.rice.edu 12
spin.rice.edu
dsp.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
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Grid Computing
• Harness global resources to improve performance
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Application: Predict Download Time
• Dynamically schedule tasks based on bandwidth availability
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Optimal Path Selection
• Choose path to minimize download time from A to D
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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 18
Chirp Probing
• Chirp: exponential flight pattern of probes
• Non-intrusive and Efficient: wide range of probing bit rates, few packets
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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 pathChirp10-90%
pathloadAvg.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 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?
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