1. 1 Turning Heterogeneity into an Advantage in Overlay Routing Gisik Kwon Dept. of Computer Science and Engineering Arizona State University Published in INFOCOM 2003 Authors: Ahichen Xu(HP), Mallik Mahalingam(VMware), Magnus Karlsson(HP)

2. Scalable Computing Lab. Arizona State University 2 Motivation Exploiting physically efficient routing and peer heterogeneity over DHT-based overlay network Constructing an auxiliary network – expressway

3. Scalable Computing Lab. Arizona State University 3 Default overlay : CAN and eCAN Each node knows its neighbors in the d-space Forward query to the neighbor that is closest to the query id Example: assume n1 queries f4 1 234 5 670 1 2 3 4 5 6 7 0 n1 n2 n3 n4 n5 f1 f2 f3 f4

4. Scalable Computing Lab. Arizona State University 4 AS-2 P2P Network AS-1 AS-3 Brocade Layer SR Original Route Brocade Route Brocade Architecture

5. Scalable Computing Lab. Arizona State University 5 Expressway Expressway nodes(EN) & expressway neighbors – Autonomous System(AS) topology – Landmark clustering Route summary – Propagated periodically – All the local nodes in same AS

6. Scalable Computing Lab. Arizona State University 6 Routing Expressway node Ordinary node

7. Scalable Computing Lab. Arizona State University 7 Experiment Stretch – The ratio of accumulated latency in the actual routing path to the shortest-path latency from the source to destination Two topology – Internet-like topology derived from BGP report – Transit-stub graph by GT-ITM Logical auxiliary – Brocade-like system

8. Scalable Computing Lab. Arizona State University 8 Comparison various approaches AS topologyTransit-stub

9. Scalable Computing Lab. Arizona State University 9 TTL and Number of ENs AS topology Transit-stub

10. 10 Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems Gisik Kwon Dept. of Computer Science and Engineering Arizona State University Published in INFOCOM 2003 Authors: Kunwadee Sripanidkulchai, Bruce Maggs, Hui Zhang (CMU) Excerpt from Kunwadee Sripanidkulchai’s presentatin file

11. Scalable Computing Lab. Arizona State University 11 Motivation Design goals – Decentralized – Simple and robust – Scalable  Let’s retain the simplicity and robustness of Gnutella and make it scalable Locality! – Network locality? No. – Popularity? No. – Interest-based locality? Yes.

12. Scalable Computing Lab. Arizona State University 12 “If a peer has a particular piece of content that I am interested in, it is very likely that it will have other pieces of content that I am (will be) interested in as well.” Interest-based locality 2002 Infocom proceedings? 2001 Infocom proceedings? Random person on the street Someone in my research group

13. Scalable Computing Lab. Arizona State University 13 Overlay on top of Gnutella Benefits – Can be easily integrated into Gnutella – Can be used with many other underlying mechanisms like DHT’s Our solution: Shortcuts

14. Scalable Computing Lab. Arizona State University 14 Discover interest-based shortcuts Where is ? No shortcut. Discover and add shortcut. Shortcut

15. Scalable Computing Lab. Arizona State University 15 Use interest-based shortcuts Where is ? Use shortcut. Success! Shortcut O(1) scope for most searches. No index (state) maintained.

16. Scalable Computing Lab. Arizona State University 16 Constructing shortcuts Shortcut discovery – Infer locality using underlying protocol (Gnutella) – Add 1 shortcut to list at a time Shortcut selection – Rank shortcuts based on performance – Ask shortcuts sequentially – Limit shortcut list size to 10

17. Scalable Computing Lab. Arizona State University 17 Trace

18. Scalable Computing Lab. Arizona State University 18 Performance of IB shortcuts

19. Scalable Computing Lab. Arizona State University 19 Removing practical limitations Shortcut discovery – Add 1 shortcut to list at a time – => add all peers returned from search – => discover shortcut through our existing shortcuts Shortcut selection – Limit shortcut list size to 10 – => no bound

20. Scalable Computing Lab. Arizona State University 20 Potential of IB shortcuts

21. 21 Measurement-Based Optimization Techniques for Bandwidth-Demanding Peer-to-Peer Systems Gisik Kwon Dept. of Computer Science and Engineering Arizona State University Published in INFOCOM 2003 Authors: T.S.Eugene Ng, Yang-hua Chu, Sanjay G. Rao, Kunwadee Sripanidkulchai, Hui Zhang

22. Scalable Computing Lab. Arizona State University 22 Motivation Improve the performance with light-weight measurement-based techniques Qualitative analysis RTT probing – Smallest response to 36B ICMP ping message 10KB TCP probing – Fastest download of 10KB data Bottleneck bandwidth probing(BNBW) – Largest nettimer – Nettimer is a project to do end-to-end network performance measurement. – It can listen passively to existing network traffic or actively probe the network.

23. Scalable Computing Lab. Arizona State University 23 Performance metrics Media file sharing – Optimality Ratio (OR) The ration between the TCP bandwidth achieved by downloading from the selected server peer and the TCP bandwidth achievable from the best server peer in the candidate set Overlay multicast streaming – Convergence time The amount of time after the initial join it takes for the peer to receive more than 95% of the stable bandwidth for 30 seconds stable bandwidth is determined based on the bandwidth it receives at the end of a 5-minutes experiment

24. Scalable Computing Lab. Arizona State University 24 Host properties

25. Scalable Computing Lab. Arizona State University 25 Accuracy of choices 36B RTT10KB TCPBNBW

26. Scalable Computing Lab. Arizona State University 26 Average OR CMU 10Mbps UIUC

27. Scalable Computing Lab. Arizona State University 27 Average OR CMU ADSL U of Alberta

28. Scalable Computing Lab. Arizona State University 28 Media file sharing Joint ranking

29. Scalable Computing Lab. Arizona State University 29 Overlay multicast streaming RTT – Single packet RTT probing RTT filter + 10K – At most 5 best RTT -> 10KB downloading RTT filter + 1-bit BNBW – At most 5 best RTT -> highest bottleneck BW

30. Scalable Computing Lab. Arizona State University 30 Mean receiver BW

31. Scalable Computing Lab. Arizona State University 31 Convergence time Basic techniquesCombined techniques