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

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

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

5. Basic Idea behind MINC o multicast probes o correlated performance observed by receivers o exploit correlation to estimate link behavior, loss rates, delay statistics, bottleneck bandwidth, available bandwidth, topology receivers source 11  33 101101 110110  ,  ,   ^^^

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

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

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

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

10. Extension: Topology Identification o given function f of node k in tree, increasing along path from root, can estimate from measurements o 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 o recursively build tree by grouping nodes, to maximize function f

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

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

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