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Popularity-Awareness in Temporal DHT for P2P-based Media Streaming Applications

Popularity-Awareness in Temporal DHT for P2P-based Media Streaming Applications. Abhishek Bhattacharya, Zhenyu Yang & Deng Pan IEEE International Symposium on Multimedia (ISM2011 ) Dana Point, California, USA December 5-7, 2011. Outline. Introduction Background Popularity-Aware Search

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Popularity-Awareness in Temporal DHT for P2P-based Media Streaming Applications

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  1. Popularity-Awareness in Temporal DHT for P2P-based Media Streaming Applications Abhishek Bhattacharya, Zhenyu Yang & Deng Pan IEEE International Symposium on Multimedia (ISM2011) Dana Point, California, USA December 5-7, 2011.

  2. Outline • Introduction • Background • Popularity-Aware Search • Estimation • Results • Summary

  3. Introduction: Distributed Hash Tables (DHT) • DHT is a generic interface • There are several implementations of this interface • Chord [MIT] • Pastry [Microsoft Research UK, Rice University] • Tapestry [UC Berkeley] • Content Addressable Network (CAN) [UC Berkeley] • SkipNet [Microsoft Research US, Univ. of Washington] • Kademlia [New York University] • Viceroy [Israel, UC Berkeley] • P-Grid [EPFL Switzerland] • Freenet [Ian Clarke]

  4. Introduction: Chord (DHT) Identifier Circle succ(x) x source pred(x) 010110000 x succ(x) 010110110 O(log n) hops for routing 010111110 Exponentially spaced pointers!

  5. Introduction: Video on Demand (VoD) c1 c2 c3 c4 c5 c6 c7 c8 p1 p4 p2 p3 p5 • Content Discovery: •  Tracking Server •  Decentralized Indexing Structures • Content Distribution: •  Overlay Tree/Multi-Tree/Mesh

  6. Outline • Introduction • Background • Popularity-Aware Search • Estimation • Results • Summary

  7. Background: DHT-based VoD System c1 :p1 c6 :p1 c2 :p1 c1 c2 c3 c4 c5 c6 c7 c8 p1 p1 p1

  8. Background: Temporal-DHT

  9. Background: Temporal-DHT … … … … pi Ci Ci Ci+1 Ci+1 Ci+2 Ci+2 Ci+z T Range Query Reformulation

  10. Outline • Introduction • Background • Popularity-Aware Search • Estimation • Results • Summary

  11. Popularity-Aware Search

  12. Popularity-Aware Search Popularity: 3 : 1 : 1 Cost: (1) log N = 4 + Range = 4 (2) log N = 4 + Range = 4 (3) log N = 4 + Range = 4 (4) log N = 4 + Range = 4 (5) log N = 4 + Range = 4 Total: 20 (excluding the common log N part)

  13. Popularity-Aware Search Popularity: 3 : 1 : 1 Cost: (1) log N = 4 + Range = 2 (2) log N = 4 + Range = 2 (3) log N = 4 + Range = 2 (4) log N = 4 + Range = 6 (5) log N = 4 + Range = 6 Total: 18 (excluding the common log N part)

  14. Outline • Introduction • Background • Popularity-Aware Search • Estimation • Results • Summary

  15. Estimation: Centralized

  16. Estimation: Decentralized 1. Local Value: xj 2. Update: xj xj + γj (xi ~ xj ) 1. Initialize xi 2. Update: xi xi-γj (xi ~ xj )

  17. Outline • Introduction • Background • Popularity-Aware Search • Estimation • Results • Summary

  18. Results: Simulation • Network Setting: • GT-ITM with 15 transit domains, each connected to 10 stub domains with 15 stub nodes each. • Data Setting: • 256 to 4096 peers with randomly distributed out/in-bound bandwidths in the range of 500~1000 Kbps. • User arrival model: Poisson distribution with λ = 1 sec • Peer Lifetime: Exponential distribution with mean of 30 mins • User Request Pattern: • 50% follow Zipf distribution with different values of α • Rest 50% with initial 6~7 random jumps followed by continuous playback mode. • Compare with VMesh, TDHTM, TDHTM -PA(α = 0.4), • TDHTM-PA (α = 2.0) • Performance Metrics: Server Stress, Streaming Quality, Messaging Overhead, Seek Latency.

  19. Results: Experiments

  20. Results: Experiments

  21. Outline • Introduction • Background • Popularity-Aware Search • Estimation • Results • Summary

  22. Summary • We incorporated the notion of popularity-awareness within the framework of a Temporal-DHT based VoD System. • Improvement of the overall performance by optimizing the search cost among the content set within the entire system. • Dynamic adaptation of the update interval based on the popularity of the content. • Decentralized computation of the popularities of various content. • Extensive simulation results demonstrate the effectiveness of the popularity awareness mechanism.

  23. Thank You........ Questions ??? Please send all your questions to: abhat002@fiu.edu

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