1. Network Coordinates ： Internet Distance Estimation Jieming ZHU 15-11-2011

2. Outline 2 Motivation Problem Statement General Approaches Applications Open Issues

3. 3 What is “Internet distance”? Round trip time –Symmetric –Relatively stable –Triangle inequality violation Bandwidth, loss rate –Not really “distance”, but useful –Asymmetric

4. 4 Why estimate distances?

5. 5 Distance estimation can be used to optimize large scale distributed systems: –Server selection –Locality aware peer-to-peer overlay networks –Application level multicast Problems with on-demand measurement: –Slow (e.g. ping N*(N-1) times) –High overhead

6. Outline 6 Motivation Problem Statement General Approaches Applications Open Issues

7. 7 Problem Statement Network Coordinates: Internet as a geometric space –Map each node to a position in the geometric space –Each host has a “coordinate” –Compute distances based on coordinates

8. Outline 8 Motivation Problem Statement General Approaches Applications Open Issues

9. 9 General Approaches Landmark-based algorithms: –Each node measure latency to set of landmark nodes –Use landmark nodes to calculate own coordinate –E.g. GNP [CMU], Lighthouses [Cambridge] Distributed algorithms: –Each node measures latency to random other nodes –Model embedding as a spring system –E.g. Vivaldi [MIT], DCS [Ottawa] Matrix factorization based algorithms –Based on SVD/NMF –E.g. IDES [Penn], Phoenix [Tsinghua]

10. 10 1. GNP: Global Network Positioning Landmark operations –Compute the coordinates of the Landmarks by minimizing: where

11. 11 1. GNP: Global Network Positioning Ordinary host operations –Ordinary host derives its own coordinates by using the coordinates of the landmarks –Simplex downhill algorithm to solve the minimization problem

12. 12 2. Vivaldi: Distributed

13. 13 2. Vivaldi: Distributed Confidence in remote node Confidence in self Adjust time step

14. 14 2. Drawback: Euclidean embedding N1 N2 N3 A B C || A || <= || B || + || C|| N1 N2 N3 100 ms 48 ms 100 <= 48 + 48 100 <= 96 TIV: Triangle Inequality Violation GNP & Vivaldi: TIV  Inaccuracy

15. 15 3. IDES: MF based

16. 16 Evaluation IDES vs. GNP Vivaldi vs. GNP

17. Outline 17 Motivation Problem Statement General Approaches Applications Open Issues

18. 18 Applications File sharing systems: find the nearest peer Database query optimization Overlay network multicast Context distribution networks Location-aware server selection Compact routing Distributed network games: find the top k nearest servers for the player

19. 19 Applications

20. Outline 20 Motivation Problem Statement General Approaches Applications Open Issues

21. 21 Open Issues Accuracy : TIV problem Scalability : Efficient (fast convergence) distributed algorithms Robustness: –Effect of network traffic –Impact of malicious nodes Stability –Vivaldi: Behavior of system when nodes are joining and leaving (node churn) –GNP: Impact of Landmarks leaving the system Applications: Web service selection

22. Thank you! Questions?