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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 Packet delay = constant term (propagation, service time) + variable term (queuing delay) End-to-end paths –Multi-hop –No packet reordering Router queues –FIFO –Constant service rate

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Available Bandwidth Unused capacity along path Available bandwidth: Goal: use end-to-end probing to estimate available bandwidth

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Applications Network monitoring Server selection Route selection (e.g. BGP) 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 Advantages –No topology information required –Robust to multiple bottlenecks TCP-Vegas uses self-induced congestion principle Probing rate < available bw no delay increase Probing rate > available bw delay increases

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Trains of Packet-Pairs (TOPP) [Melander et al] Vary sender packet-pair spacing Compute avg. receiver packet-pair spacing Constrained regression based estimate Shortcoming: packet-pairs do not capture temporal queuing behavior useful for available bandwidth estimation Packet-pairs Packet train

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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 I.Per-packet pair available bandwidth, (k=packet number) II.Per-chirp available bandwidth III.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 Valid excursion decreasing queuing delay Last excursion 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 SLAC Rice and Chicago Rice paths Estimates fall in proportion to introduced Poisson traffic

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Comparison with TOPP 30% utilization Equal avg. probing rates for pathChirp and TOPP Result: pathChirp outperforms TOPP 70% utilization

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Comparison with Pathload 100Mbps links pathChirp uses 10 times fewer bytes for comparable accuracy Available bandwidth EfficiencyAccuracy pathchirppathloadpathChirp 10-90% pathload Avg.min-max 30Mbps0.35MB3.9MB19-29Mbps16-31Mbps 50Mbps0.75MB5.6MB39-48Mbps39-52Mbps 70Mbps0.6MB8.6MB54-63Mbps63-74Mbps

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Conclusions Chirp trains –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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