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pathChirp Efficient Available Bandwidth Estimation
Vinay Ribeiro Rice University Rolf Riedi Jiri Navratil Rich Baraniuk Les Cottrell (Rice) (SLAC)
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Network Model End-to-end paths Router queues Multi-hop
No packet reordering Router queues FIFO Constant service rate Packet delay = constant term (propagation, service time) + variable term (queuing delay)
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Available Bandwidth Available bandwidth:
Unused capacity along path Available bandwidth: Goal: use end-to-end probing to estimate available bandwidth
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Applications Server selection Route selection (e.g. BGP)
Network monitoring SLA verification Congestion control
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Available Bandwidth Probing Tool Requirements
Fast estimate within few RTTs Unobtrusive introduce light probing load Accurate No topology information (e.g. link speeds) Robust to multiple congested links No topology information (e.g. link speeds) Robust to multiple congested links
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Principle of Self-Induced Congestion
Probing rate < available bw no delay increase Probing rate > available bw delay increases Advantages No topology information required Robust to multiple bottlenecks TCP-Vegas uses self-induced congestion principle
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Trains of Packet-Pairs (TOPP) [Melander et al]
Shortcoming: packet-pairs do not capture temporal queuing behavior useful for available bandwidth estimation Vary sender packet-pair spacing Compute avg. receiver packet-pair spacing Constrained regression based estimate Packet train Packet-pairs
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Pathload [Jain & Dovrolis]
CBR packet trains Vary rate of successive trains Converge to available bandwidth Shortcoming Efficiency: only one data rate per train
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Chirp Packet Trains Exponentially decrease packet spacing within packet train Wide range of probing rates Efficient: few packets
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Chirps vs. Packet-Pairs
Each chirp train of N packets contains N-1 packet pairs at different spacings Reduces load by 50% Chirps: N-1 packet spacings, N packets Packet-pairs: N-1 packet spacings, 2N-2 packets Captures temporal queuing behavior
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Chirps vs. CBR Trains Multiple rates in each chirping train
Allows one estimate per-chirp Potentially more efficient estimation
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CBR Cross-Traffic Scenario
Point of onset of increase in queuing delay gives available bandwidth
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Bursty Cross-Traffic Scenario
Goal: exploit information in queuing delay signature
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PathChirp Methodology
Per-packet pair available bandwidth, (k=packet number) Per-chirp available bandwidth Smooth per-chirp estimate over sliding time window of size
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Self-Induced Congestion Heuristic
Definitions: delay of packet k inst rate at packet k
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Excursions Must take care while using self-induced congestion principle Segment signature into excursions from x-axis Valid excursions are those consisting of at least “L” packets Apply only to valid excursions
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Setting Per-Packet Pair Available Bandwidth
Valid excursion increasing queuing delay Last excursion Valid excursion decreasing queuing delay Invalid excursions
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pathChirp Tool UDP probe packets
No clock synchronization required, only uses relative queuing delay within a chirp duration Computation at receiver Context switching detection User specified average probing rate open source distribution at spin.rice.edu
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Performance with Varying Parameters
Vary probe size, spread factor Probing load const. Mean squared error (MSE) of estimates Result: MSE decreases with increasing probe size, decreasing spread factor
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Multi-hop Experiments
First queue is bottleneck Compare No cross-traffic at queue 2 With cross-traffic at queue 2 Result: MSE close in both scenarios
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Internet Experiments 3 common hops between SLACRice and ChicagoRice paths Estimates fall in proportion to introduced Poisson traffic
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Comparison with TOPP Equal avg. probing rates for pathChirp and TOPP
Result: pathChirp outperforms TOPP 30% utilization 70% utilization
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Comparison with Pathload
100Mbps links pathChirp uses 10 times 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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Conclusions Chirp trains pathChirp Experiments
Probe at multiple rates simultaneously Efficient estimates pathChirp Self-induced congestion Excursion detection Experiments Internet experiments promising Large probe packet size, small spread factor better Outperforms existing tools open-source code is available at spin.rice.edu Demo during 10:30a.m. break
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