11 Experimental and Analytical Evaluation of Available Bandwidth Estimation Tools Cesar D. Guerrero and Miguel A. Labrador Department of Computer Science & Engineering University of South Florida

22 Outline Motivation Problem Testbed Description Analytical Model Target Tools Performance Evaluation Conclusions

33 Motivation Why to evaluate available bandwidth tools? Available bandwidth  to improve network applications performance. Applications  different time, accuracy, and overhead from estimators. Evaluation  determine whether a tool is suitable for an application.

44 Problem What issues do we want to solve? Evaluate tools over the same variable network conditions Analytical model to have a theoretical value to compare with  Topology  Link capacities  Packet loss rate  Delay

55 Testbed Description Architecture Client and server hosting bandwidth estimation tools Intermediate nodes hosting a packet shaper and a traffic generator Phython applications running in all the machines to automatically perform experiments. Internet connected Low cost

66 Analytical Model Jackson Network Eight M/M/1 queues model input and output packet flows. The Jackson model gives the average arrival rate to a node λ j = γ j + Σ λ i θ ij The available bandwidth is the minimum non utilized capacity of the queues associated to the links: A = min i=1,3,5,7 (A i ) = min i=1,3,5,7 (1-ρ i ) ClientServer Cross Traffic λ0λ0 λ1λ1 λ2λ2 λ3λ3 λ4λ4 λ5λ5 λ7λ7 λ6λ6 γ1γ1 γ3γ3 γ5γ5 γ7γ7 λ8λ8 Probing packets

77 Target Tools Estimation Approaches Probe Rate Model Pathload. TOPP Pathchirp PTR Probe Gap Model IGI Delphy Spruce IGIPathload Spruce

88 Target Tools Pathload Fleet of probing streams are sent to fill the available bandwidth. The one-way delay increases when the rate of the probing traffic is higher that the available bandwidth. In the gray region, the tool reports the available bandwidth grey region Figure copied from the paper “Pathload: A Measurement Tool for End-to-end Available Bandwidth” by M. Jain and C. Dovrolis

99 Target Tools IGI Estimates the cross traffic as a function of the amount of traffic inserted between a packet pair. Available bandwidth is given by the average rate of the packet train when the initial packet gap is equal to the output gap. turning point Figure copied from the paper “Evaluation and Characterization of Available Bandwidth Probing Techniques” by N. Hu and P. Steenkiste

10 Target Tools Spruce Probing packets are sent with an intra-pair gap (Δ in ) equal to the narrow link transmission time of a 1500B packet (to guarantee that the pair will be in the queue at the same time) Cross traffic is measured using the dispersion of the probing packets (Δ out ) calculated at the receiver. It requires a previous calculation of the tight link capacity (C)

11 Performance Evaluation Experiments Metrics: accuracy, time, overhead 28 network scenarios: link capacities from 1 to 10 Mbps and from 10 to 100 Mbps Each scenario with four cross traffic loads: 0%, 25%, 50%, and 75% of the capacity Every estimation was performed 35 times Accuracy plots have a 95% confidence interval experiments

12 Performance Evaluation Accuracy with 75% of the capacity as cross traffic Estimated available bandwidth / total bandwidth (capacity) PathloadIGISpruce

13 Performance Evaluation Relative Error PathloadIGISpruce

14 Performance Evaluation Convergence Time PathloadIGISpruce

15 Performance Evaluation Overhead PathloadIGISpruce Probing traffic / total bandwidth (capacity) in the tight link

16 Conclusions Main contributions: Low cost and flexible testbed to evaluate estimation tools in a controlled network. Analytical model to fairly compare the tools accuracy with a theoretical value. Regarding to the tools evaluation: Pathload is the most accurate tool but the slowest to converge IGI is the fastest tool but the least accurate Spruce is the least intrusive tool with intermediate accuracy and convergence time.

17 Experimental and Analytical Evaluation of Available Bandwidth Estimation Tools Cesar D. Guerrero Miguel A. Labrador Department of Computer Science & Engineering University of South Florida