CapProbe: An Efficient Capacity Estimation Technique for Internet Paths

1. CapProbe: An Efficient and Accurate Capacity Estimation TechniqueSigcomm 2004 Rohit Kapoor**, Ling-Jyh Chen*, Li Lao*, M.Y. Sanadidi*, Mario Gerla* ** Qualcomm Corp R&D *UCLA Computer Science Department

2. The Capacity Estimation Problem • Estimate minimum link capacity on an Internet path, as seen at the IP level • Design Goals • End-to-end: assume no help from routers • Inexpensive: Minimal additional traffic and processing at end nodes • Fast: converges to capacity fast enough for the application

3. Applications • TCP parameters optimization • Adaptive multimedia streaming • Overlay network structuring • Wireless link monitoring and mobility detection

4. 20Mbps 10Mbps 5Mbps 10Mbps 20Mbps 8Mbps T1 Narrowest Link T2 T3 T3 T3 T3 Packet Pair Dispersion and Capacity

5. Ideal Packet Dispersion • FIFO routers, no cross-traffic Capacity = (Packet Size) / (Dispersion)

6. Expansion of Dispersion • Cross-traffic (CT) serviced between PP packets • Second packet queues due to Cross Traffic (CT )=> expansion of dispersion =>Under-estimation

7. Compression of Dispersion • First packet queueing => compressed dispersion => Over-estimation

8. Previous Work • Dovrolis’ Work • Analyzed under/over estimation of capacity • Designed Pathrate • First send packet pairs • If multimodal, send packet trains • Identifies modes to distinguish ADR (Asymptotic Dispersion Rate), PNCM (Post Narrow Capacity Mode) and Capacity Modes • Previously proposed techniques have relied either on dispersion or delay

9. Key Observation • First packet queues (post bottle-neck) • Compression • Capacity over-estimation • Second packet queues (pre or post b-neck) • Expansion • Under-estimation • Both over and under estimation are the result of probe packets experiencing queuing delay • E-to-E delay min when queuing delay = 0

10. CapProbe Approach • Filter PP results that have queuing time > 0, ie not the minimum E to E delay • Dispersion of PP with “minimum delay sum” (of the two packets in the pair) reflects capacity • CapProbe combines both dispersion and e2e transit delay information

11. Techniques for Convergence Detection • Consider set of packet pair probes 1…n • If min(d1) + min(d2) ≠ min(d1+d2), dispersion obtained from min delay sum may be distorted • Above condition increases correct detection probability to that of a single packet (as opposed to packet pair) • If above minimum delay sum condition is not satisfied in a run • New run, with packet size of probes • Increased if bandwidth estimated varied a lot across probes • Decreased if bandwidth estimated varied little across probes

12. Bandwidth Estimate Frequency Over-Estimation Cross Traffic Rate Simulations • 6-hop path: capacities {10, 7.5, 5.5, 4, 6, 8} Mbps • PP pkt size = 200 bytes, CT pkt size= 1000 bytes • Persistent TCP Cross-Traffic Minimum Delay Sums Cross Traffic Rate

13. Bandwidth Estimate Frequency Under-Estimation Simulations • PP pkt size = CT pkt size = 500 bytes • Non-Persistent TCP Cross-Traffic Minimum Delay Sums

14. Simulations Bandwidth Estimate Frequency • Non-Persistent UDP CBR Cross-Traffic • Case where CapProbe does not work • UDP (non-responsive), extremely intensive • No correct samples are obtained Minimum Delay Sums

15. CapProbe • Sufficient requirement • At least one PP sample where both packets experience no CT induced queuing delay. • How realistic is this requirement? • Internet is reactive (mostly TCP): high chance of some probe packets not being queued • To validate, we performed extensive experiments • Only cases where such samples are not obtained is when cross-traffic is UDP and very intensive (typically >75% load)

16. Probability of Obtaining Sample Second Packet First Packet Link No Queue • Assuming PP samples arrive in a Poisson manner • Product of probabilities • No queue in front of first packet: p(0) = 1 – λ/μ • No CT packets enter between the two packets (worst case) • Only dependent on arrival process • p = p(0) * e- λL/μ = (1 – λ/μ) * e- λL/μ • Analysis also for deterministic and Pareto cross-traffic No Cross Traffic Packets

17. Probability of Obtaining Sample (cont) Avg number of samples required to obtain an unqueued PP for a single link; Poisson cross-traffic Avg number of samples required to obtain an unqueued PP for a single link; LRD cross-traffic

18. Experiments • Simulations, Internet, Internet2 (Abilene), Wireless • TCP (responsive), CBR (non-responsive), LRD (Pareto) cross-traffic • Wireless technologies tested were Bluetooth, IEEE 802.11, 1xRTT • Path-persistent, non-persistent cross-traffic

19. Internet Measurements • Each experiment: 500 PP at 0.5s intervals • 100 experiments for each {Internet path, nature of CT, narrow link capacity}

20. Issues • CapProbe may be implemented either in the kernel or user mode • Kernel mode more accurate, particularly over high-speed links • One-way or round-trip estimation • One-way • Requires cooperation from receiver • Can be used to estimate forward/reverse link • Active vs passive • Heavy cross-traffic • Difficulty in correct estimation

21. Summary • CapProbe is accurate, fast, and inexpensive, across a wide range of scenarios • Potential applications in overlay structuring, and in case of fast changing wireless link speeds • High-speed dispersion measurements needs more investigation