1. Bandwidth estimation in computer networks: measurement techniques & applications Constantine Dovrolis College of Computing Georgia Institute of Technology

2. The Internet as a “ black box ” Several network properties are important for applications and transport protocols: Delay, loss rate, jitter, capacity, congestion, load, etc But, routers and switches do not provide direct feedback to end-hosts (except ICMP, also of limited use) Mostly due to scalability, policy, and simplicity factors

3. Can we guess what is in the black box? End-systems can infer network state through end-to-end (e2e) measurements Without any feedback from routers Objectives: accuracy, speed, non-intrusiveness probe packets

4. Bandwidth estimation in packet networks Bandwidth estimation (bwest): Inference of various throughput-related metrics with end-to-end network measurements Early works: Keshav ’ s packet pair method for congestion control ( ‘ 89) Bolot ’ s capacity measurements ( ’ 93) Carter-Crovella ’ s bprobe and cprobe tools ( ’ 96) Jacobson ’ s pathchar – per hop capacity estimation ( ‘ 97) Melander ’ s TOPP avail-bw estimation ( ’ 00) In last ± 5 years: Several new estimation techniques Many research papers at prestigious conferences More than a dozen of new measurement tools Several applications of bwest methods Significant commercial interest in bwest technology

5. Overview This talk is an overview of the most important developments in bwest area over the last 10 years A personal bias could not be avoided.. Overview Bandwidth-related metrics Capacity estimation Packet pairs and CapProbe technique Available bandwidth estimation Iterative probing: Pathload and PathChirp Direct probing: Spruce Comparison of tools Percentile estimation: Pathvar Applications Overlay-based Video Streaming SOcket Buffer Auto-Sizing (SOBAS)

6. Network bandwidth metrics Ravi S.Prasad, Marg Murray, K.C. Claffy, Constantine Dovrolis, “ Bandwidth Estimation: Metrics, Measurement Techniques, and Tools, ” IEEE Network, November/December 2003.

7. Capacity definition Maximum possible end-to-end throughput at IP layer In the absence of any cross traffic Achievable with maximum-sized packets If C i is capacity of link i, end-to-end capacity C defined as: Capacity determined by narrow link

8. Available bandwidth definition Per-hop average avail-bw: A i = C i (1-u i ) u i : average utilization A.k.a. residual capacity End-to-end avg avail-bw A: Determined by tight link ISPs measure per-hop avail-bw passively (router counters, MRTG graphs)

9. The avail-bw as a random process Instantaneous utilization u i (t): either 0 or 1 Link utilization in (t, t+  ) Averaging timescale:  Available bandwidth in (t, t+  ) End-to-end available bandwidth in (t, t+  )

10. Available bandwidth distribution Avail-bw has significant variability Need to estimate second-order moments, or even better, the avail-bw distribution Variability depends on averaging timescale  Larger timescale, lower variance Distribution is Gaussian-like, if  >100-200 msec and with sufficient flow multiplexing

11. E2E Capacity estimation (a ’ ): Packet pair technique Constantine Dovrolis, Parmesh Ramanathan, David Moore, “ Packet Dispersion Techniques and Capacity Estimation, ” In IEEE/ACM Transactions on Networking, Dec 2004.

12. Packet pair dispersion Packet Pair (P-P) technique Originally, due to Jacobson & Keshav Send two equal-sized packets back-to-back Packet size: L Packet trx time at link i: L/C i P-P dispersion: time interval between last bit of two packets Without any cross traffic, the dispersion at receiver is determined by narrow link:

13. Cross traffic interference Cross traffic packets can affect P-P dispersion P-P expansion: capacity underestimation P-P compression: capacity overestimation Noise in P-P distribution depends on cross traffic load Example: Internet path with 1Mbps capacity

14. Multimodal packet pair distribution Typically, P-P distribution includes several local modes One of these modes (not always the strongest) is located at L/C Sub-Capacity Dispersion Range (SCDR) modes: P-P expansion due to common cross traffic packet sizes (e.g., 40B, 1500B) Post-Narrow Capacity Modes (PNCMs): P-P compression at links that follow narrow link

15. E2E Capacity estimation (b ’ ): CapProbe Rohit Kapoor, Ling-Jyh Chen, Li Lao, Mario Gerla, M. Y. Sanadidi, "CapProbe: A Simple and Accurate Capacity Estimation Technique," ACM SIGCOMM 2004

16. Compression of p-p dispersion First packet queueing => compressed dispersion Capacity overestimation

17. Expansion of p-p dispersion Second packet queueing => expansion of dispersion Capacity underestimation

18. CapProbe Both expansion and compression result from queuing delays Key insight: packet pair with zero queueing delay would yield correct estimate measure RTT for each probing packet of packet pair estimate capacity from pair with minimum RTT-sum Capacity Capacity

19. Measurements - Internet, Internet2 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 6’56’55.70 ’ 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

20. Avail-bw estimation (a ’ ): Pathload Manish Jain, Constantine Dovrolis, “ End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput, ” IEEE/ACM Transactions on Networking, Aug 2003.

21. Probing methodology Sender transmits periodic packet stream of rate R K packets, packet size L, interarrival T = L/R Receiver measures One-Way Delay (OWD) for each packet D(k) = t arv (k) - t snd (k) OWD variations: Δ(k) = D(k+1) – D(k) Independent of clock offset between sender/receiver With stationary & fluid-modeled cross traffic: If R > A, then Δ(k) > 0 for all k Else, Δ(k) = 0 for all k

22. Self-loading periodic streams Increasing OWDs means R>A Almost constant OWDs means R<A

23. Example of OWD variations 12-hop path from U-Delaware to U-Oregon K=100 packets, A=74Mbps, T=100μsec R left = 97Mbps, R right =34Mbps Ri

24. Iterative probing and Pathload Iterative probing in Pathload: Send multiple probing streams (duration τ ) at various rates Each stream samples path at different time interval Outcome of each stream is either R i > A or R i < A Estimate upper and lower bound for avail-bw variation range Avail-bw time series from NLANR trace

25. Avail-bw estimation (b ’ ): PathChirp Vinay Ribeiro, Rolf Riedi, Jiri Navratil, Rich Baraniuk, Les Cottrell, “ PathChirp: Efficient Available Bandwidth Estimation, ” PAM 2003

26. Chirp Packet Trains Exponentially decrease packet spacing within packet train Wide range of probing rates Efficient: few packets

27. Chirps vs. CBR Trains Multiple rates in each chirping train Allows one estimate per-chirp Potentially more efficient estimation

28. Comparison with Pathload 100Mbps links pathChirp uses 10 times fewer bytes for comparable accuracy Available bandwidth EfficiencyAccuracy pathchirppathloadpathChirp 10-90% pathload Avg.min-max 30Mbps0.35MB3.9MB19-29Mbps16-31Mbps 50Mbps0.75MB5.6MB39-48Mbps39-52Mbps 70Mbps0.6MB8.6MB54-63Mbps63-74Mbps

29. Avail-bw estimation (c ’ ): Direct probing & Spruce Jacob Strauss, Dina Katabi, and Frans Kaashoek “ A Measurement Study of Available Bandwidth Estimation Tools, ” IMC 2003

30. Direct probing Iterative methods are relatively slow and they need to probe at multiple rates Direct probing methods Probe only once at a rate R i and determine avail- bw from output rate R o Spruce (IMC ’ 03) followed this approach With a single-link fluid model (capacity C t ): Sender sends periodic stream with input rate R i Receiver measures output rate R o

31. What ’ s wrong with Direct Probing? Two major issues: Tight link capacity C t is assumed to be known What if tight link is different than narrow link? Input rate R i equal to source rate (set equal to C t ) But what would happen in a multi-hop path? Probing stream can arrive at tight link with lower rate than source rate (due to a link with lower avail-bw than C t before the tight link) Results in avail-bw underestimation For details, see CCR paper, Oct 2006, by Lao, Dovrolis and Sanadidi,  “ The Probe Gap Model can Underestimate the Available Bandwidth of Multihop Paths ”

32. Avail-bw estimation (d ’ ): percentile estimation Manish Jain, Constantine Dovrolis, “ End-to-End Estimation of the Available Bandwidth Variation Range, ” ACM SIGMETRICS 2005

33. Avail-bw distribution and percentiles Avail-bw random process, in timescale  A  (t) Assume stationarity Marginal distribution of A  : F  (R) = Prob [A  ≤ R] A p :p th percentile of A , such that p = F  (A p ) Objective: Estimate variation range [A L, A H ] for given averaging timescale  A L  and A H are p L and p H percentiles of A  Typically, p L =0.10 and p H =0.90

34. Percentile sampling Question : Which percentile of A  corresponds to rate R? Given R and  estimate F  (R) Assume that F  (R) is inversible Sender transmits periodic packet stream of rate R Length of stream = measurement timescale  Receiver classifies stream as Type-G if A  ≤ R: I(R)= 1 with probability F  (R) Type-L otherwise: I(R)= 0 with probability 1-F  (R) Collect N samples of I(R) by sending N streams of rate R Number of type-G streams: I(R,N)= Σ i I i (R) E[ I(R,N) ] = F  (R) * N Percentile rank of probing rate R: F  (R) = E[I(R,N)] / N Use I(R,N)/N as estimator of F  (R)

35. Non-parametric estimation Does not assume specific avail-bw distribution Iterative algorithm Stationarity requirement across iterations N-th iteration: probing rate R n Use percentile sampling to estimate percentile rank of R n To estimate the upper percentile A H with p H = F  (A H ): f n = I(R n,N)/N If f n is between p H ±  report A H = R n Otherwise, If f n > p H + , set R n+1 < R n If f n R n Similarly, estimate the lower percentile A L

36. Validation example Verification using real Internet traffic traces b=0.05 b=0.15 Non-parametric estimator tracks variation range within 10-20% Selection of b depends on traffic Traffic spikes/dips may not be detected if b is too small But larger b causes larger MSRE

37. Parametric estimation Assume Gaussian avail-bw distribution Justified assumption for large degree of traffic multiplexing And/or for long averaging timescale (>200msec) Gaussian distribution completely specified by Mean  and standard deviation   p th percentile of Gaussian distribution A p =  +    -1 (p) Sender transmits N probing streams of rates R 1 and R 2 Receiver determines percentiles ranks corresponding to R 1 and R 2  and   can be then estimated by solving R 1 =  +    -1 (p 1 ) R 2 =  +    -1 (p 2 ) Variation range is then calculated from: A H =  +    -1 (p H ) A L =  +    -1 (p L )

38. Validation example Gaussian trafficnon-Gaussian traffic Verification using real Internet traffic traces Evaluated with both Gaussian and non-Gaussian traffic Parametric algorithm is more accurate than non-parametric algorithm, when traffic is good match to Gaussian model in non-stationary conditions

39. Experimental comparison of avail-bw estimation tools Alok Shriram, Marg Murray, Young Hyun, Nevil Brownlee, Andre Broido, k claffy, ” Comparison of Public End-to-End Bandwidth Estimation Tools on High-Speed Links, ” PAM 2005

40. Testbed topology

41. Accuracy comparison - SmartBits Direction 1, Measured AB Direction 2, Measured AB Actual AB

42. Accuracy comparison - TCPreplay Actual Available Bandwidth Measured Available Bandwidth

43. Measurement latency comparison Abing: 1.3 to 1.4 s Spruce: 10.9 to 11.2 s Pathload: 7.2 to 22.3 s Patchchirp: 5.4 s Iperf: 10.0 to 10.2 s

44. Probing traffic comparison

45. Applications of bandwidth estimation

46. Large TCP transfers and congestion control Bandwidth-delay product estimation Socket buffer sizing Streaming multimedia Adjust encoding rate based on avail-bw Intelligent routing systems Overlay networks and multihoming Select best path based on capacity or avail-bw Content Distribution Networks (CDNs) Choose server based on least-loaded path SLA and QoS verification Monitor path load and allocated capacity End-to-end admission control Network “ spectroscopy ” Several more..

47. Overlay-based Video Streaming M. Jain and C. Dovrolis, “ Path Selection using Available Bandwidth Estimation in Overlay-based Video Streaming, ” published at IFIP Networking 2007.

48. Motivation Video/IPTV next “ killer-application ” in the Internet IP networks present several challenges Network impairments such as losses and jitter Lack of QoS guarantees Previous approaches Adapt encoding rate Video quality not consistent Use proactive error correction techniques such as forward error correction (FEC) or retransmission Bandwidth overhead for FEC Potentially larger playback delay Error concealment techniques Limited applicability & effectiveness

49. Exploiting Path Diversity Multihoming and overlay networks make multiple paths available between sender and receiver Applications can dynamically switch from one path to another Based on observed (or predicted) performance Serves as an additional adaptation mechanism Schemes using path diversity based on network measurement have been explored Path selection techniques: Tao et al. [ACM Multimedia ‘ 04] and Amir et al. [NOSSDAV ‘ 05] Multi-description coding techniques: Begen et al. [Signal Processing ‘ 05] and Apostolopoulos et al. [INFOCOM ‘ 02] They use only loss and jitter measurements Employ dummy packets for measurement

50. Our Approach We consider use of overlay network for video streaming To maximize perceived video quality Novel features in our approach: Use avail-bw to drive dynamic path selection Use VQM technique instead of PSNR to measure video quality Described in ITU-T recommendation J.144 Use data packets to substitute “ dummy ” packets for network measurements Objective: Evaluate the performance of different path selection schemes in terms of perceived video quality

51. Overlay based Video Streaming Overlay nodes run measurement module (m-module) To measure network performance Video stream delivered via at most two-hop path Based on results by Zhu, Dovrolis and Ammar, 2006 M-module uses active measurements to estimate Loss-rate Jitter Avail-bw: average and variation range Uses video packets for in-band measurements In paths with video streams

52. Self Loading Periodic Streams (SLoPS) Objective: Determine whether avail-bw A is greater or smaller than a given rate R A > R or A <= R Sender transmits periodic packet stream of rate R K packets, packet size L, departure spacing T = L/R Receiver measures One-Way Delay (OWD) for each packet D(k) = t arv (k) - t snd (k) OWD variations: Δ(k) = D(k+1) – D(k) Independent of clock offset between sender/receiver With stationary & fluid-modeled cross traffic: If R > A, then Δ(k) > 0 for all k Else, Δ(k) = 0 for all k

53. In-band Avail-bw Estimation In-band avail-bw estimation uses video packets for probing network paths Ingress M-module shapes the incoming probing packets to rate R p If incoming rate R v < R p  Add decreasing amount of delay  i Else, add increasing amount of delay  i Insert  i in packet header Egress M-module re-shapes the video stream to rate R v using  i

54. Path Selection schemes We consider 4 path selection schemes Based on choice of measured network performance metric Loss based (LPS) Loss-rate monitored during 3 second period Path with lowest loss-rate selected Jitter based (JPS) Jitter of successive packet pairs measured in 3 second period Path with minimum 90 th percentile of jitter measurements selected  If jitter is same, then path with lowest loss-rate is selected Avail-bw based Average avail (A-APS) Lower bound of avail-bw variation range (L-APS) In current path:  If avail-bw < twice of video rate; then switch to path with maximum avail-bw

55. Experimental Setup Testbed details: Video stream transmitted from node B to E A 2-minute MPEG-2 encoded clip from SMPTE test video sequences M-module on nodes B, D, and E Cross traffic generated by replaying NLANR packet traces

56. Video Quality Estimation Performance of path selection schemes evaluated based on: Video quality Measured using VQM tool  http://www.its.bldrdoc.gov/n3/video/vqmsoftware.htm http://www.its.bldrdoc.gov/n3/video/vqmsoftware.htm VQM compares the original video stream with the received video stream Lower VQM score implies higher quality User-abort probability Based on VQM scores of 10-second video segments Path switching frequency

57. Results: Experiment 1 L-APS performs better than other schemes A-APS performance similar to LPS Average avail-bw does not capture the variability of avail-bw distribution Ranking of 4 schemes in terms of abort fraction is similar to long term VQM scores

58. Results: Experiment 1 L-APS has lowest path switching frequency JPS has higher path switching frequency than L-APS Similar path switching frequency to A-APS

59. Results: Experiment 2 Avail-bw based path selection results in better perceived video quality Compared to algorithms relying on jitter or loss-rate

60. Path Switching versus FEC We compared performance of simplest form of Reed-Solomon FEC code (n, k) n-k out of n packets carry FEC packets In our experiments n = k+1 L-APS performs consistently better than FEC

61. Summary Explored the use of measurement driven path selection schemes for video streaming Avail-bw estimates based path selection shows better performance Compared to other network metrics An interesting next step would be to use real-time video quality measurements to drive path selection schemes Also, hybrid schemes involving use of both path selection and FEC is unexplored

62. Improved TCP Throughput with BwEst Ravi S. Prasad, Manish Jain, Constantine Dovrolis, “ Socket Buffer Auto-Sizing for High-Performance Data Transfers, ” Journal of Grid Computing, 2003

63. TCP performs poorly in fat-long paths Basic idea in TCP congestion control: Increase congestion window until packet loss occurs Then reduce window by a factor of two (multiplicative decrease) Consequences Increased loss-rate Avg. throughput less than available bandwidth Large delay-variation Example: 1Gbps path, 100msec RTT, 1500B pkts Single loss ≈ 4000 pkts window reduction Recovery from single loss ≈ 400 sec Need very small loss probability (ρ<2*10 -8 ) to saturate path

64. Can we avoid congestive losses w/o changing TCP? W s = min {W c, W r } But W r <= S S: rcv socket buffer size We can limit S so that connection is limited by receive window: W s = S Non-congested path: Throughput R(S) = S/T(S) R(S) increases with S until it reaches MFT MFT: Maximum Feasible Throughput (onset of congestion) Objective: set S close to MFT, to avoid any losses and stabilize TCP send window to a safe value Congested path: Path with losses before start of target transfer Limiting S can only reduce throughput

65. Socket buffer auto-sizing (SOBAS) Apply SOBAS only in non-congested paths Receiving application measures rcv-throughput R rcv When receive throughput becomes constant: Connection has reached its maximum throughput R max If the send window becomes any larger, losses will occur Receiving application limits rcv socket buffer size S: S = RTT * R max With congestion unresponsive cross traffic: R max = A With congestion responsive cross traffic: R max = MFT SOBAS does not require any changes in TCP But estimation of RTT and congestion-status requires “ ping-like ” probing

66. Measurements at WAN path Path: GaTech to LBL,CA SOBAS flow get higher average throughput than non-SOBAS flow because former avoids all congestion losses SOBAS does not significantly increase path’s RTT Non-SOBAS SOBAS

67. To conclude..

68. Things I have not talked about Other estimation tools & techniques ABget, pipechar, STAB, pathneck, IGI/PTR, … Per-hop capacity estimation (pathchar, clink, pchar) Other applications Bwest in new TCPs (e.g., TCP Westwood, TCP FAST, PCP) Bwest in wireless nets Bwest in multihoming Bwest in bottleneck detection Theoretical analysis of packet pair/train dispersion Unavoidable estimation bias in avail-bw measurements due to traffic burstiness Diminishes as packet train length increases Scalable bandwidth measurements in networks with many monitoring nodes Practical issues Interrupt coalescence Traffic shapers Non-FIFO queues

69. Conclusions We know how to do the following: Estimate e2e capacity & avail-bw Apply bwest in several applications We know that we cannot do the following: Estimate per-hop capacity with VPS techniques Avoid avail-bw estimation bias when traffic is bursty and/or path has multiple bottlenecks We do not yet know how to do the following: Scalable but accurate bandwidth measurements Integrate with more applications Many research questions are still open Help wanted!