pathChirp Efficient Available Bandwidth Estimation Vinay Ribeiro Rice University Rolf Riedi Jiri Navratil Rich Baraniuk Les Cottrell (Rice) (SLAC)
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)
Available Bandwidth Available bandwidth: Unused capacity along path Available bandwidth: Goal: use end-to-end probing to estimate available bandwidth
Applications Server selection Route selection (e.g. BGP) Network monitoring SLA verification Congestion control
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
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
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
Pathload [Jain & Dovrolis] CBR packet trains Vary rate of successive trains Converge to available bandwidth Shortcoming Efficiency: only one data rate per train
Chirp Packet Trains Exponentially decrease packet spacing within packet train Wide range of probing rates Efficient: few packets
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
Chirps vs. CBR Trains Multiple rates in each chirping train Allows one estimate per-chirp Potentially more efficient estimation
CBR Cross-Traffic Scenario Point of onset of increase in queuing delay gives available bandwidth
Bursty Cross-Traffic Scenario Goal: exploit information in queuing delay signature
PathChirp Methodology Per-packet pair available bandwidth, (k=packet number) Per-chirp available bandwidth Smooth per-chirp estimate over sliding time window of size
Self-Induced Congestion Heuristic Definitions: delay of packet k inst rate at packet k
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
Setting Per-Packet Pair Available Bandwidth Valid excursion increasing queuing delay Last excursion Valid excursion decreasing queuing delay Invalid excursions
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
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
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
Internet Experiments 3 common hops between SLACRice and ChicagoRice paths Estimates fall in proportion to introduced Poisson traffic
Comparison with TOPP Equal avg. probing rates for pathChirp and TOPP Result: pathChirp outperforms TOPP 30% utilization 70% utilization
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
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