1. Tomography-based Overlay Network Monitoring and its Applications Joint work with David Bindel, Brian Chavez, Hanhee Song, and Randy H. Katz UC Berkeley Yan Chen

2. Motivation Applications of end-to-end distance monitoring –Overlay routing/location –Peer-to-peer systems –VPN management/provisioning –Service redirection/placement –Cache-infrastructure configuration Requirements for E2E monitoring system –Scalable & efficient: small amount of probing traffic –Accurate: capture congestion/failures –Incrementally deployable –Easy to use

3. Existing Work Static estimation: –Global Network Positioning (GNP) Dynamic monitoring –Loss rates: RON (n 2 measurement) –Latency: IDMaps, Dynamic Distance Maps, Isobar Latency similarity under normal conditions doesn’t imply similar losses ! Network tomography –Focusing on inferring the characteristics of physical links rather than E2E paths –Limited measurements -> under-constrained system, unidentifiable links

4. Problem Formulation Given n end hosts on an overlay network and O(n 2 ) paths, how to select a minimal subset of paths to monitor so that the loss rates/latency of all other paths can be inferred. Key idea: select a basis set of k paths that completely describe all O(n 2 ) paths (k «O(n 2 )) –Select and monitor k linearly independent paths to compute the loss rates of basis set –Infer the loss rates of all other paths –Applicable for any additive metrics, like latency End hosts Overlay Network Operation Center topology measurements

5. Path Matrix and Path Space Path loss rate p, link loss rate l Totally s links, path vector v Path matrix G –Put all r = O(n 2 ) paths together –Path loss rate vector b A p B ljlj

6. Sample Path Matrix x 1 - x 2 unknown => cannot compute x 1, x 2 Set of vectors form null space To separate identifiable vs. unidentifiable components: x = x G + x N All E2E paths are in path space, i.e., Gx N = 0 A D C B 1 2 3 b1b1 b2b2 b3b3 (1,-1,0) link 2 link 1 link 3 (1,1,0) row space (measured) null space (unmeasured)

7. 12 1 23 1’ Real links (solid) and overlay paths (dotted) going through them Virtualization Virtual links 1’2’ 1 2 3 1’ 2’ 4 Rank(G)=1 Rank(G)=2 Rank(G)=3 3’ 4’ Intuition through Topology Virtualization 1 1 2 1 2 3 Virtual links: minimal path segments whose loss rates uniquely identified Can fully describe all paths x G : similar forms as virtual links 5

8. Algorithms Select k = rank(G) linearly independent paths to monitor –Use rank revealing decomposition, e.g., QR with column pivoting –Leverage sparse matrix: time O(rk 2 ) and memory O(k 2 ) E.g., 10 minutes for n = 350 (r = 61075) and k = 2958 Compute the loss rates of other paths –Time O(k 2 ) and memory O(k 2 )

9. How much measurement saved ? k « O(n 2 ) ? For power-law Internet topology, M nodes, N end hosts –There are O(M) links and N > M/2 (with proof) –If n = O(N), overlay network has O(n) IP links, k = O(n)

10. When a Small Portion of End Hosts on Overlay Internet has moderate hierarchical structure [TGJ+02] –If a pure hierarchical structure (tree): k = O(n) –If no hierarchy at all (worst case, clique): k = O(n 2 ) –Internet should fall in between … For reasonably large n, (e.g., 100), k = O(nlogn) BRITE 20K-node hierarchical topology Mercator 284K-node real router topology

11. Practical Issues Topology measurement errors tolerance –Care about path loss rates than any interior links –Poor router alias resolution present show multiple links for one => assign similar loss rates to all the links –Unidentifiable routers => ignore them as virtualization Topology changes –Add/remove/change one path incurs O(k 2 ) time –Topology relatively stable in order of a day => incremental detection

12. Evaluation Simulation –Topology BRITE: Barabasi-Albert, Waxman, hierarchical: 1K – 20K nodes Real router topology from Mercator: 284K nodes –Fraction of end hosts on the overlay: 1 - 10% –Loss rate distribution (90% links are good) Good link: 0-1% loss rate; bad link: 5-10% loss rates Good link: 0-1% loss rate; bad link: 1-100% loss rates –Loss model: Bernouli: independent drop of packet Gilbert: busty drop of packet –Path loss rate simulated via transmission of 10K pkts Metric: path loss rate estimation accuracy –Absolute/relative errors –Lossy path inference

13. Areas and Domains # of hosts US (40).edu33.org3.net2.gov1.us1 Interna- tional (11) Europe (6) France1 Sweden1 Denmark1 Germany1 UK2 Asia (2) Taiwan1 Hong Kong1 Canada2 Australia1 Experiments on Planet Lab 51 hosts, each from different organizations –51 × 50 = 2,550 paths Simultaneous loss rate measurement –300 trials, 300 msec each –In each trial, send a 40-byte UDP pkt to every other host Simultaneous topology measurement –Traceroute Experiments: 6/24 – 6/27 –100 experiments in peak hours

14. Loss rate distribution Accuracy –On average k = 872 out of 2550 –Absolute error |p – p’|: Average 0.0027 for all paths, 0.0058 for lossy paths –Relative error loss rate [0, 0.05) lossy path [0.05, 1.0] (4.1%) [0.05, 0.1)[0.1, 0.3)[0.3, 0.5)[0.5, 1.0)1.0 %95.9%15.2%31.0%23.9%4.3%25.6% Tomography-based Overlay Monitoring Results

15. Absolute and Relative Errors For each experiment, get its 95 percentile absolute and relative errors for estimation of 2,550 paths

16. Lossy Path Inference Accuracy 90 out of 100 runs have coverage over 85% and false positive less than 10% Many caused by the 5% threshold boundary effects

17. Topology Measurement Error Tolerance Out of 13 sets of pair-wise traceroute … On average 248 out of 2550 paths have no or incomplete routing information No router aliases resolved Conclusion: robust against topology measurement errors

18. Performance Improvement with Overlay With single-node relay Loss rate improvement –Among 10,980 lossy paths: –5,705 paths (52.0%) have loss rate reduced by 0.05 or more –3,084 paths (28.1%) change from lossy to non-lossy Throughput improvement –Estimated with –60,320 paths (24%) with non-zero loss rate, throughput computable –Among them, 32,939 (54.6%) paths have throughput improved, 13,734 (22.8%) paths have throughput doubled or more Implications: use overlay path to bypass congestion or failures

19. X UC Berkeley UC San Diego Stanford HP Labs Adaptive Overlay Streaming Media Implemented with Winamp client and SHOUTcast server Congestion introduced with a Packet Shaper Skip-free playback: server buffering and rewinding Total adaptation time < 4 seconds

20. Adaptive Streaming Media Architecture

21. Conclusions A tomography-based overlay network monitoring system –Given n end hosts, characterize O(n 2 ) paths with a basis set of O(nlogn) paths –Selectively monitor O(nlogn) paths to compute the loss rates of the basis set, then infer the loss rates of all other paths Both simulation and real Internet experiments promising Built adaptive overlay streaming media system on top of monitoring services –Bypass congestion/failures for smooth playback within seconds

22. Backup Slides