1. pathChirp Spatio-Temporal Available Bandwidth Estimation Vinay Ribeiro Rolf Riedi, Richard Baraniuk Rice University

2. A Bird’s Eye View of the Internet Data transmitted as packets Multiple routers on end-to-end path Routers queue bursts of packets

3. Edge-based Probing Internet provides connectivity Lack of optimization Difficult to obtain information from routers Solution: inject probe packets to measure internal properties

4. Questions awaiting Answers What does the Internet topology look like? Where does congestion occur and why? Given several mirror sites to download data which one to choose? Is my ISP honoring the service-level agreement?

5. Available Bandwidth Estimation

6. Network Model Packet delay = constant term (propagation, service time) + variable term (queuing delay) End-to-end paths –Multi-hop –No packet reordering Router queues –FIFO –Constant service rate

7. Available Bandwidth Unused capacity along path Available bandwidth: Goal: use end-to-end probing to estimate available bandwidth

8. Applications Network monitoring Server selection Route selection (e.g. BGP) SLA verification Congestion control

9. Available Bandwidth Probing Tool Requirements Fast estimate within few RTTs Unobtrusive introduce light probing load Accurate No topology information (e.g. link speeds) Robust to multiple congested links No topology information (e.g. link speeds) Robust to multiple congested links

10. Principle of Self-Induced Congestion Advantages –No topology information required –Robust to multiple bottlenecks TCP-Vegas uses self-induced congestion principle Probing rate < available bw  no delay increase Probing rate > available bw  delay increases

11. Trains of Packet-Pairs (TOPP) [Melander et al] Vary sender packet-pair spacing Compute avg. receiver packet-pair spacing Constrained regression based estimate Shortcoming: packet-pairs do not capture temporal queuing behavior useful for available bandwidth estimation Packet-pairs Packet train

12. Pathload [Jain & Dovrolis] CBR packet trains Vary rate of successive trains Converge to available bandwidth Shortcoming Efficiency: only one data rate per train

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

14. Chirps vs. Packet-Pairs Each chirp train of N packets contains N-1 packet pairs at different spacings Reduces load by 50% –Chirps: N-1 packet spacings, N packets –Packet-pairs: N-1 packet spacings, 2N-2 packets Captures temporal queuing behavior

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

16. CBR Cross-Traffic Scenario Point of onset of increase in queuing delay gives available bandwidth

17. Bursty Cross-Traffic Scenario Goal: exploit information in queuing delay signature

18. PathChirp Methodology I.Per-packet pair available bandwidth, (k=packet number) II.Per-chirp available bandwidth III.Smooth per-chirp estimate over sliding time window of size

19. Self-Induced Congestion Heuristic Definitions: delay of packet k inst rate at packet k

20. Excursions Must take care while using self-induced congestion principle Segment signature into excursions from x-axis Valid excursions are those consisting of at least “L” packets Apply only to valid excursions

21. Setting Per-Packet Pair Available Bandwidth Valid excursion increasing queuing delay Valid excursion decreasing queuing delay Last excursion Invalid excursions

22. pathChirp Tool UDP probe packets No clock synchronization required, only uses relative queuing delay within a chirp duration Computation at receiver Context switching detection User specified average probing rate open source distribution at spin.rice.edu

23. Performance with Varying Parameters Vary probe size, spread factor Probing load const. Mean squared error (MSE) of estimates Result: MSE decreases with increasing probe size, decreasing spread factor

24. Multi-hop Experiments First queue is bottleneck Compare –No cross-traffic at queue 2 –With cross-traffic at queue 2 Result: MSE close in both scenarios

25. Internet Experiments 3 common hops between SLAC  Rice and Chicago  Rice paths Estimates fall in proportion to introduced Poisson traffic

26. Comparison with TOPP 30% utilization Equal avg. probing rates for pathChirp and TOPP Result: pathChirp outperforms TOPP 70% utilization

27. 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

28. Tight Link Localization

29. Key Definitions Goal: use end-to-end probing to locate tight link in space and over time Path available bandwidth Sub-path available bandwidth Tight link: link with least available bandwidth

30. Applications Network monitoring - locating hot spots Network aware applications - server selection Science: where do Internet tight links occur and why?

31. Methodology Estimate A[1,m] For m>tight link, A[1,m] remains constant

32. Principle of Self-Induced Congestion Probing rate = R, path available bandwidth = A Advantages –No topology information required –Robust to multiple bottlenecks R < A  no delay increase R > A  delay increases

33. Packet Tailgating Large packets of size P (TTL=m) small packets of size p Large packets exit at hop m Small packets reach receiver with timing information Previously employed in capacity estimation

34. Estimating A[1,m] Key: Probing rate decreases by p/(p+P) at link m Assumption: r<A[m+1,N], no delay change after link m R < A[1,m]  no delay increase R > A[1,m]  delay increases

35. Tight Link Localization Tight link: link after which A[1,m] remains constant Applicable to any self-induced congestion tool: pathload, pathChirp, IGI, netest etc.

36. ns-2 Simulation Heterogeneous sources Tight link location changes over time pathChirp tracks tight link location change accurately tight link estimate

37. Internet Experiment Two paths: UIUC  Rice and SLAC  Rice Paths share 4 common links Same tight link estimate for both paths SLAC  Rice tight link UIUC  Rice tight link

38. Comparison with MRTG Data A[1,m] decreases as expected Tight link location differs from MRTG data by 1 hop SLAC  RiceUIUC  Rice

39. High Speed Probing System I/O limits probing rate On high speed networks:  cannot estimate A using self-induced congestion

40. Receiver System I/O Limitation Treat receiver I/O bus as an extra link Use packet tailgating If then we can estimate A[1,N-1]

41. Sender System I/O Limitations Combine sources to increase net probing rate Issue: machine synchronization

42. Conclusions Towards spatio-temporal available bandwidth estimation Combine self-induced congestion and packet tailgating Available bandwidth and tight link localization in space and over time ns-2 and Internet experiments encouraging Solutions to system I/O bandwidth limitations spin.rice.edu