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Manish Jain Constantinos Dovrolis SIGCOMM 2002

End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput. Manish Jain Constantinos Dovrolis SIGCOMM 2002. Presented by Jyothi Guntaka. Definitions.

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Manish Jain Constantinos Dovrolis SIGCOMM 2002

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  1. End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput Manish Jain Constantinos Dovrolis SIGCOMM 2002 Presented by Jyothi Guntaka

  2. Definitions • Path capacity C: Maximum possible end-to-end throughput. It is defined as C = mini=0…H {Ci}, where, Ci is capacity of link i. • Available bandwidth (termed as avail-bw): Spare capacity in the path. In other terms, maximum end-to-end throughput given cross traffic load. It is a time-varying metric, defined as average over a certain time interval. • Narrow link: The link with minimum capacity. • Tight link: The link with minimum available bandwidth.

  3. Capacity vs. Avail-bw

  4. Previous work • Measure throughput of bulk TCP transfer • A bulk TCP’s throughput is not avail-bw. • TCP saturates path (i.e., intrusive measurements) • Carter & Crovella: dispersion of long packet trains (cprobe) • Ribeiro et al.: estimation technique for single-queue paths (Delphi) • Melander et al.: attempt to estimate capacity & avail-bw of every link in path (TOPP)

  5. Self-Loading Periodic Streams (SLoPS) • Basic idea: • Periodic stream (probing packets) which consists of K packets of size L at a constant rate R is sent from sender to receiver. • When R>A, the one-way delays of successive packets at the receiver show an increasing trend.

  6. SLoPS (2) • Periodic stream: K packets, period T, packet size L, rate: R=L/T

  7. SND RCV SLoPS with Fluid Cross Traffic • For a path P: • One-way delay (OWD) of packet k where is the queue size at link i upon k’s arrival SLoPS Stream Cross Traffic

  8. SLoPS with Fluid Cross Traffic (2) • The OWD difference between two successive packets k and k+1 is: where • Proposition 1: if R > A, then for k=1,…,K-1. Else, if R < A, for k=1,…,K-1

  9. SLoPS algorithm • Iterative algorithm • Sender send a periodic stream n at rate R(n) • Receiver determine whether or not R(n) > A • Receiver notify sender: • If R(n) > A, R(n+1) < R(n) • Else, R(n+1) > R(n) • Specifically: • Initially: • If R(n) > A, then • The algorithm terminate when :

  10. Check with Proposition 1 • A=74Mbps (MRTG), R=96Mbps (K=100packets, T=100s, L=1200B) R=96 Mbps R = 37 Mbps

  11. Refinement of SLoPS algorithm R=82 Mbps • Refinement: • Watching the increasing • trend during the entire • stream • Accept the possibility of • variation of A during a • probing stream, no strict • ordering between R and A • which is called • grey-region

  12. PATHLOAD: Implementation • No timing issue: consider the variation of OWD • Parameters: • a stream consists of K packets, each has size L, sent at a constant rate R, inter-spacing time T = L/R, • Stream duration V=KT

  13. Detection of increasing OWD trend • OWD of a stream, can be grouped into groups, find median in each group , Pathload analyzes the set • Two metrics to determine the trend • Pairwise Comparison Test (PCT) • PCT: Measures the fraction of consecutive OWD pairs that are increasing (between 0 and 1).

  14. Detection of increasing OWD trend (2) • Pairwise Difference Test (PDT) • PDT: Quantifies how strong is the start-to-end OWD variation, relative to the OWD absolute variations during the stream (between –1 and 1).

  15. Interval  between streams max { RTT, 9V } One Stream V=KT N streams in a fleet at a single iterative step N_default = 12 Fleets of streams • N streams • idle time between streams • Duration of a fleet • Average rate of a fleet = packets

  16. Rate-adjustment algorithm • If either metrics shows an increasing trend, the stream is typed as type-I, otherwise type-N. • If a fraction f of the streams in a fleet are type-I, the fleet has a rate > A. • If a fraction f of the streams in a fleet are type-N, the fleet has a rate < A. • If less than Nf streams are type-I, and also less than Nf streams are type-N, then the fleet is in grey-region.

  17. Grey region • Measurement stream rate can fall into avail-bw variation range. • Pathload reports grey-region boundaries [Gmin, Gmax]. • Relative width of grey-region: quantify avail-bw variability.

  18. Experimental Verification • Simulation scenario: • Path tightness factor:

  19. Simulation Results • Pathload produces a range that includes the average avail-bw in the path, in both light and heavy load conditions at the tight link.

  20. Simulation Results (2) • Pathload estimates a range that includes the actual avail-bw in all cases, independent of the number of non-tight links or of their load.

  21. Simulation Results (3) • Pathload succeeds in estimating a range that includes the actual avail-bw when there is only one tight link in the path, but it underestimates the avail-bw where there are multiple tight links.

  22. Dynamics of Available Bandwidth • Relative variation metrics: • To compare the variability of the avail-bw across different operating conditions and paths. • Each experiment has 110 runs, plot the {5,15,…,95} percentiles of .

  23. Different Load Condition • Variability of the avail-bw increases significantly as the utilization u of the tight link increases (i.e., as the avali-bw A decreases).

  24. Effect of Stream Length K • Variability of the avail-bw decreases significantly as the stream duration increases.

  25. Effect of Fleet Length • As the fleet duration increases, the variability in the measured avail-bw increases. Also, as the fleet duration increases, the variation across different pathload runs decreases.

  26. TCP and intrusiveness • A Bulk Transfer Capacity (BTC) connection using TCP can get more bandwidth than what was previously available in the path, grabbing part of the throughput of other TCP connections. • Pathload is not intrusive.

  27. TCP and intrusiveness (2)

  28. TCP and intrusiveness (3)

  29. Applications • Bandwidth-Delay-Product in TCP • Overlay networks and end-system multicast • Rate adaptation in streaming applications • End-to-end admission control • Server selection and anycasting

  30. Comments • Works well when there is only one tight link. • Almost all parameters are empirical. • Could be difficult to tune them under different scenarios. • Difficult to draw general conclusions. • Difficult to predict converge time. • In their reported experiments, converge time for a single fleet of streams is [10, 30] seconds. • Not intrusive? • Only gives a single experiment. Difficult to justify. • How about if lots of users are using pathloads?

  31. Acknowledgements • Some of the slides are taken from • The presentation by Honggang Zhang (http://gaia.cs.umass.edu/measurement/slides/avbw.ppt) • http://lion.cs.uiuc.edu/seminar.ppt

  32. Questions?

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