Network Tomography through End-End Multicast Measurements

1. Network Tomography through End-End Multicast Measurements D. Towsley U. Massachusetts collaborators: R. Caceres, N. Duffield, F. Lo Presti (AT&T) T. Bu, J. Horowitz, J. Kurose, S. Moon (UMass) V. Paxson (ACIRI)

2. LOSS % Delay LOSS% 1 2 3 4 5 6 1 2 3 4 5 6 4 5 6 Avg. Delay 4 5 6 infrastructure 6 6 1 1 cross section view cross section view composition of views cross section view ? 2 2 4 4 3 3 5 5 Avg. Delay LOSS % 1 2 3 1 2 3 Network Tomography Goal: obtain detailed picture of a network/internet from end-to-end views ?

3. Ingredients • end-end measurement methodology • determine internal behavior from one view • composition from multiple views • view layout • measurement infrastructure • hardware platforms • software • experiment setup • trace collection • analysis and display

4. MINC (Multicast Inference of Network Characteristics Point to point measurements (snapshots) • good for end-end performance metrics but not link behavior Why? lack of correlation  use multicast end-end measurements

5. a1 a2 a3 1 0 1 1 1 0 ^ ^ ^ a1, a2, a3 Basic Idea behind MINC source • multicast probes • correlated performance observed by receivers • exploit correlation to estimate link behavior • loss rates • delay statistics • bottleneck bandwidth • available bandwidth • topology receivers

6. Inference Methodology (Losses) • model • known multicast tree topology T =(V,E) • mtrace • independent Bernoulli processes across all links with unknown loss prob. ak for link k V • methodology • maximum likelihood estimates for {ak} • asymptotic consistency • optimality • robust to relaxation of independence assumption • validated against simulation (2-8 rcvrs), measurements

7. Scatter Plots • 2-8 rcvrs, TCP/UDP background traffic • deterministic and Poisson probes background loss vs inf. loss probe loss vs inferred loss inferred loss background loss probe loss • good agreement between inferred and probe loss • increased variability between probe and background loss

8. experiments with 2- 8 receivers (100ms probes) summer ‘98 topology determined using mtrace validation against mtrace kentucky atlanta cambridge SF edgar erlang LA saona WO conviction excalibur alps rhea MINC: Mbone Results

9. complement of CDF compl. of CDF ... ... ... ... Delay (ms) Delay (ms) Extensions: Link Delays • link delay distribution • Dk integer valued, k V, independent between links • estimator is extension of MLE for loss • estimator performance (23 receiver tree) • delay variance (simpler estimators)

10. Extension: Topology Identification • given function f of node k in tree • increasing along path from root • can estimate from measurements • examples • Prob[probe lost on path from root 0 to k] • average delay from root to node k • delay variance from root to node k • recursively build tree by grouping nodes • to maximize function f

11. Open Problems • relation to unicast • how to compose multiple views • how to layout multiple views • reduce number of trees? • reduce probe traffic? • integrate existing tools (mtrace, ...)

12. Infrastructure • hardware platforms • National Internet Measurement Infrastructure (NIMI): 20 - 25 platforms • Surveyor? 50+ platforms • routers with added functionality? • software infrastructure • NIMI (zing, natalie) generates traces • RTCP for performance reports • MRM (multicast route monitor) • MINT for analysis and visualization

13. Summary • design for network tomography based on end2end measurements • multicast to introduce correlation • losses, delays, topology discovery • efficient estimators exist for link metrics • several possible infrastructures • NIMI + RTCP + MINT http://gaia.cs.umass.edu/minc