1. CapProbe: An Efficient and Accurate Capacity Estimation Technique 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 End-to-end: assume no help from routers Inexpensive: Minimal additional traffic and processing Inexpensive: Minimal additional traffic and processing Fast: converges to capacity fast enough for the application Fast: converges to capacity fast enough for the application 100 Mbps 50 Mbps 10 Mbps (Link Capacity)

3. Applications  Adaptive multimedia streaming  Congestion control  Capacity planning by ISPs  Overlay network structuring  Wireless link monitoring and mobility detection

4. Packet Pair Dispersion T3T3 T2T2 T3T3 T3T3 T1T1 T3T3 Narrowest Link 20Mbps10Mbps5Mbps10Mbps20Mbps8Mbps

5. Ideal Packet Dispersion  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  More pronounced when CT pkt size < probe pkt size

7. Compression of Dispersion  First packet queueing => compressed dispersion => Over-estimation  More pronounced when CT pkt size > probe pkt size

8. Previous Work  Jacobson’s Pathchar Estimates capacity for every link Estimates capacity for every link Sends varying size packets Sends varying size packets Relies on round trip delays Relies on round trip delays  Packet Pairs (PP) Crovella Crovella Capacity is reflected by the packet pair dispersion that occurs with highest frequencyCapacity is reflected by the packet pair dispersion that occurs with highest frequency Lai Lai Filters samples whose dispersion reflects a capacity greater than their “potential bandwidth”Filters samples whose dispersion reflects a capacity greater than their “potential bandwidth” Both these techniques assume unimodal distribution Both these techniques assume unimodal distribution Paxson showed distribution can be multimodal Paxson showed distribution can be multimodal

9. Previous Work  Dovrolis’ Work Analyzed under/over estimation of capacity Analyzed under/over estimation of capacity Designed Pathrate Designed Pathrate First send packet pairsFirst send packet pairs If multimodal, send packet trainsIf multimodal, send packet trains Identifies modes to distinguish ADR (Asymptotic Dispersion Rate), PNCM (Post Narrow Capacity Mode) and Capacity ModesIdentifies 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

10. Key Observation First packet queues more than the second First packet queues more than the second  Compression  Over-estimation Second packet queues more than the first Second packet queues more than the first ExpansionExpansion Under-estimationUnder-estimation Both expansion and compression are the result of probe packets experiencing queuing Both expansion and compression are the result of probe packets experiencing queuing Sum of PP delay includes queuing delaySum of PP delay includes queuing delay

11. CapProbe Approach  Filter PP samples that do not have minimum queuing time  Dispersion of PP sample with minimum delay sum reflects capacity  CapProbe combines both dispersion and e2e transit delay information

12. 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 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)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 New run, with packet size of probes Increased if bandwidth estimated varied a lot across probesIncreased if bandwidth estimated varied a lot across probes Errors in dispersion measured by OS Errors in dispersion measured by OS Decreased if bandwidth estimated varied little across probesDecreased if bandwidth estimated varied little across probes Packet sizes too large to go through without queuing Packet sizes too large to go through without queuing

13. Experiments  Simulations  TCP (responsive), CBR (non-responsive), LRD (Pareto) cross-traffic  Path-persistent, non-persistent cross-traffic

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

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

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

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

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

19. 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

20. Effect of Packet Size on Accuracy  For CapProbe to estimate accurately Neither packet of the PP should queue due to cross traffic Neither packet of the PP should queue due to cross traffic  Second packet of PP Smaller  less chances of queuing due to cross-traffic Smaller  less chances of queuing due to cross-traffic  First packet of PP Probability of queuing independent of size (queuing theory) Probability of queuing independent of size (queuing theory)  Thus, smaller PP packets  higher probability of sample not subject to queuing  Previous authors (Dovrolis) have shown that Smaller packets reduce chances of under-estimation but increase chances of over-estimation Smaller packets reduce chances of under-estimation but increase chances of over-estimation

21. Effect of Packet Size on Accuracy  Our observations are entirely consistent with earlier ones For the second packet, smaller packet size  Smaller probability of being queued  Relative probability of queuing of first packet is increased  Chances of over- estimation are increased For the second packet, smaller packet size  Smaller probability of being queued  Relative probability of queuing of first packet is increased  Chances of over- estimation are increased Frequency of occurrence of bandwidth samples when packet size of probes is (a) 100 and (b) 1500 bytes

22. Measurements- Internet, Internet2 (Abilene), Wireless (802.11, Bluetooth) To UCLA-2UCLA-3UANTNU TimeCapacityTimeCapacityTimeCapacityTimeCapacity Cap Probe 0’035.50’01960’02980’0797 0’035.60’01970’04790’0797 0’035.50’02970’17830’2297 Pathrate 6’105.60’16985’19860’2997 6’145.40’16985’20880’2597 6’55.70’16985’181330’2597 Pathchar 21’124.022’49183 hr343 hr34 20’433.927’41183 hr343 hr35 21.184.029’47183 hr303 hr35 CapProbe implemented using PING packets, sent in pairs

23. Issues  CapProbe may be implemented either in the kernel or user mode Kernel mode more accurate, particularly over high- speed links Kernel mode more accurate, particularly over high- speed links  One-way or round-trip estimation One-way One-way Requires cooperation from receiverRequires cooperation from receiver Can be used to estimate forward/reverse linkCan be used to estimate forward/reverse link  Active vs passive Probing packets or data packets used as probes Probing packets or data packets used as probes  Heavy cross-traffic/extremely fast links Difficulty in correct estimation Difficulty in correct estimation

24. 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  CapProbe website: http://nrl.cs.ucla.edu/CapProbe