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Turning Heterogeneity into an Advantage in Overlay Routing

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

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Turning Heterogeneity into an Advantage in Overlay Routing

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

  2. Motivation • Exploiting physically efficient routing and peer heterogeneity over DHT-based overlay network • Constructing an auxiliary network • expressway

  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 7 6 n5 n4 n3 f4 5 4 f1 3 n2 n1 2 f3 1 f2 0 0 2 3 4 6 7 5 1

  4. Brocade Architecture Brocade Layer Original Route Brocade Route AS-3 AS-1 S R AS-2 P2P Network

  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. Routing Expressway node Ordinary node

  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. Comparison various approaches AS topology Transit-stub

  9. TTL and Number of ENs Transit-stub AS topology

  10. Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems Published in INFOCOM 2003 Authors: Kunwadee Sripanidkulchai, Bruce Maggs, Hui Zhang (CMU) Excerpt from Kunwadee Sripanidkulchai’s presentatin file Gisik Kwon Dept. of Computer Science and Engineering 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. Interest-based locality Someone in my research group Random person on the street “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.” 2002 Infocom proceedings? 2001 Infocom proceedings?

  13. Our solution: Shortcuts • Overlay on top of Gnutella • Benefits • Can be easily integrated into Gnutella • Can be used with many other underlying mechanisms like DHT’s

  14. Shortcut Where is ? Discover and add shortcut. Discover interest-based shortcuts No shortcut.

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

  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. Trace

  18. Performance of IB shortcuts

  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. Potential of IB shortcuts

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

  25. Accuracy of choices 36B RTT 10KB TCP BNBW

  26. Average OR UIUC CMU 10Mbps

  27. Average OR U of Alberta CMU ADSL

  28. Media file sharing • Joint ranking

  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. Mean receiver BW

  31. Convergence time Basic techniques Combined techniques

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