1. Lo Presti 1 Network Tomography Francesco Lo Presti Dipartimento di Informatica - Università dell’Aquila

2. Lo Presti 2 Tomography  Noninvasive imaging technique  X-ray beam directed through one part of the body onto a film/sensors  Computer reconstructs cross sectional views of the body from data received from the sensors

3. Lo Presti 3 Network Tomography  Motivation: Network Optimization Problem Given G=(N,E) and a weight function w…  Who knows N, E or w? ?

4. Lo Presti 4 Network Tomography  Motivation: Network Optimization Problem ­ Given G=(N,E) and a cost function c…  Who knows G, N, E or c? ? ATTSprint British Telecom Telecom Italia

5. Lo Presti 5 Network Tomography  Characterize internal network characteristics from end-to-end packet behavior

6. Lo Presti 6 Network Tomography  Characterize internal network characteristics from end-to-end packet behavior

7. Lo Presti 7 Network Tomography 1 2 3 4 5 LOSS % 1 2 3 4 5 Avg. Delay 1 2 3 4 5 inference engine infer topology

8. Lo Presti 8 Network Tomography  Why end-to-end ­ decentralized stateless network ­ ISPs do not share internal measurements  Applications ­ network performance monitoring network managementnetwork management researchresearch overlay networks, CDNoverlay networks, CDN  Key Ingredients ­ measurement infrastructure ­ measurement/inference techniques 1 2 3 4 5

9. Lo Presti 9 Key Ingredients  Network Model ­ topology, loss, delay model, etc. ­ parameterized by a suitable vector   Goal: Infer   Probing Technique ­ active, passive traffic ­ unicast, multicast packet pairs, stripes, cartouches, etc.packet pairs, stripes, cartouches, etc.  Measurements ­ receivers observations X distribution is function of distribution is function of   Inference Technique ­ MLE, Bayesian, Method-of-Moments, etc. 1 2 3 4 5

10. Lo Presti 10 Identifiability  captures the property that a particular internal network characteristic can be uniquely identified from a given type of measurements (as the # of probes goes to  ) 1 2 3 4 5

11. Lo Presti 11 Probing, Measurements and Inference  Unicast probes ­ infer path characteristics E.g., available bandwidthE.g., available bandwidth 1 2 3 4 5

12. Lo Presti 12 Probing, Measurements and Inference  Multicast probes  exploit correlation observed at receivers ­ infer Mcast tree segments loss/delay characteristics Segment: Path between branch points/source-recv & a branch pointSegment: Path between branch points/source-recv & a branch point ­ logical tree topology  Assumptions ­ independent loss/delay spatial correlationspatial correlation »bias temporal correlationtemporal correlation »slower convergence ­ stationary behavior 1 2 3 4 5

13. Lo Presti 13 Probing, Measurements and Inference  Multicast probes & Multiple Trees ­ increase network cover ­ increase # of links which can be identified 1 2 3 4 5

14. Lo Presti 14 Probing, Measurements and Inference  Unicast pairs (back-to-back packets sent to different receivers ­ assimilated to the corresponding 2-recvs Mcast tree case ­ imperfect correlation leads to bias ­ better use longer sequences (Stripes)  To cover the net use ­ different receivers ­ different senders 1 2 3 4 5

15. Lo Presti 15 Inference Techniques  Maximum Likelihood ­ L(X;  )= likelihood of the recvs observations X ­ Estimate  = argmax  L(X;  ) Loss prob.: closed form expressionsLoss prob.: closed form expressions Delay distr.: EM AlgorithmDelay distr.: EM Algorithm Topology: exhaustive, Markov Chain Monte CarloTopology: exhaustive, Markov Chain Monte Carlo  MLE Properties ­ asymptotic consistency ­ asymptotic normality ­ asymptotically efficient  Bayesian ­ mcast tree topology  Greedy Heuristics ­ mcast tree topology  Method of Moments ­ delay cumultants  …

16. Lo Presti 16 Simulations Results: Loss  ns simulations  Good agreement between inferred and actual loss with 1000~2000 probes

17. Lo Presti 17 Simulation Results: Delay  ns simulations  complement of cdf of link delay  complement of cdf of link delay D k (~18000 probes)  Good agreement between inferred and actual distributions P[D k  delay] Delay (ms) k D(k)

18. Lo Presti 18 Network Tomography: the Challenge  Can Network Tomography support online network operation and applications? ­ network monitoring, SLA monitoring, CDN, …  Self Monitoring Networks  Which extensions? ­ Infer network characteristics more closely related to protocols/applications behaviors Multiple layers to account forMultiple layers to account for ­ Wireless/Ad-hoc Networks ­ Scalability, scalability, scalability!!!

19. Lo Presti 19 Network Tomography: Challenges  Richer yet parsimonious network models ­ overcome simplifying models assumptions correlation, non stationary, second order statistics, mobility, etc.correlation, non stationary, second order statistics, mobility, etc.  Probing theory/framework ­ explore probing techniques space grouping destination, size, timing, TTL, scopinggrouping destination, size, timing, TTL, scoping ­ tailor probing to protocols/applications of interest ­ passive traffic  Novel approaches to inference ­ statistics theory/alg. tailored to networking

20. Lo Presti 20 Challenges: Distributed Measurement Infrastructure  Issues: ­ scale: cooperation, data exchange ­ delay measurements accuracy, ­ we can only infer attributes of a network we can “surround” 1 2 3 4 5

21. Lo Presti 21 Challenges: Distributed Measurement Infrastructure  Single Node end-to-end Measurements ­ relies on TTL expiry techniques ­ no cooperation ­ no data exchange  Allows performance inference along both direction of traversed links ­ need symmetric paths  Intuition: We still rely on a logical tree topology where sender and receivers coincide!! Network

22. Lo Presti 22 Application: SLA Monitoring  Application : SLA Monitoring AS Peering points Measurements Node

23. Lo Presti 23 Summary  Network Tomography based on end-to-end measurements  Challenges ­ Modeling ­ Probing techniques ­ Inference ­ Measurements