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. 2 Definitions  Path capacity C: Maximum possible end-to-end throughput. It is defined as C = min i=0…H {C i }, where, C i 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. 3 Capacity vs. Avail-bw

4. 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. 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. 6 SLoPS (2)  Periodic stream: K packets, period T, packet size L, rate: R=L/T

7. 7 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 SNDRCV SLoPS Stream Cross Traffic

8. 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. 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. 10 Check with Proposition 1  A=74Mbps (MRTG), R=96Mbps (K=100packets, T=100  s, L=1200B) R=96 MbpsR = 37 Mbps

11. 11 Refinement of SLoPS algorithm 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 R=82 Mbps

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

16. 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. 17 Grey region  Measurement stream rate can fall into avail-bw variation range.  Pathload reports grey-region boundaries [G min, G max ].  Relative width of grey-region: quantify avail-bw variability.

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

19. 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. 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. 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. 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. 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. 24 Effect of Stream Length K  Variability of the avail-bw decreases significantly as the stream duration increases.

25. 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. 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. 27 TCP and intrusiveness (2)

28. 28 TCP and intrusiveness (3)

29. 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. 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. 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. 32 Questions?