1 / 24

Prediction-based Prefetching to Support VCR-like Operations in Gossip-based P2P VoD Systems

Prediction-based Prefetching to Support VCR-like Operations in Gossip-based P2P VoD Systems. Tianyin Xu , Weiwei Wang, Baoliu Ye Wenzhong Li, Sanglu Lu, Yang Gao Nanjing University. Outline. Background P2P VoD streaming; Gossip-based systems; VCR-like interactive behavior.

atara
Download Presentation

Prediction-based Prefetching to Support VCR-like Operations in Gossip-based P2P VoD Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Prediction-based Prefetching to Support VCR-like Operations in Gossip-based P2P VoD Systems TianyinXu, Weiwei Wang, Baoliu Ye Wenzhong Li, SangluLu, Yang Gao Nanjing University Dislab, NJU CS

  2. Outline • Background • P2P VoD streaming; Gossip-based systems; VCR-like interactive behavior. • Motivation • Solutions • System architecture; Prefetching model; Data scheduling; VCR-like operation support. • Performance Evaluation • Conclusions Dislab, NJU CS

  3. Background (1) • P2P media streaming • Everyone can be a content producer/provider. • Cache-and-relay mechanism: peers actively cache media contents and further relay them to other peers that are expecting them. * P2P live streaming is very successful! • CoolStreaming (INFOCOM’05), • PPLive, Joost Dislab, NJU CS

  4. Background (2) • P2P VoD streaming is challenging! • Provide free access to any segment in the video at anytime by VCR-like operations. • VCR-like (Video Cassette Recorder) operations • random seek, pause, fast forward/backward (FF/FB) • For VCR-like operations, “jump” process is the most important. • Most VCR-like operations can be implemented by “jump”. • random seek & pause: 1 jump; FF/FB: series of jump; Dislab, NJU CS

  5. Question: Is the prediction feasible? Motivation (1) • How to support the “jump”? • Optimizing the index overlay to realize fast segment relocation • Jump => locate-and-download process; • Necessary, but far more sufficient. • Prediction-based Prefetching • Expect a zero jump delay; • Proactively prefetch segments that are likely to be requested by future VCR-like operations; • Rely on prediction accuracy. Dislab, NJU CS

  6. User Access Patterns (1) • User rarely view the movie from the beginning to the end. • The total playing time of a user is quite limited and tends to be short. • Because some popular segments (called highlights) attract more user requests than non-popular segments. Brampton et al., NOSSDAV-2007 Zheng et al., P2PMMS-2005 Dislab, NJU CS

  7. User Access Patterns (2) • Probability distribution of object and segment popularity • Log-normal distribution • Zipf distribution Brampton et al., NOSSDAV-2007 Yu et al., EUROSYS-2006 Dislab, NJU CS

  8. User Access Patterns (3) • Fast Forward is more frequent than Fast Backward. • Short Jump is more frequent than Long Jump. Cheng et al., IPTPS-2007 Brampton et al., NOSSDAV-2007 Dislab, NJU CS

  9. Motivation (2) • Our Objective: Effective Prediction-based Prefetching Scheme • Effective prediction model • Based on user access patterns • Easy to be integrated in current P2P VoD systems • Practical data scheduling Dislab, NJU CS

  10. Our solution: • Server side: offline pattern mining => prediction model • Peer side: lightweight online prediction System Architecture (1) • Solution 1: Let the server do prediction for each user [1] • Pro: Server has large volumes of user viewing logs • Con: poor scalability • Solution 2: Let the client exchange user logs and do prediction [2] • Pro: scalable • Cons: 1. lack of large volumes of user logs 2. high computing cost & training time [1] Huang et al, “A User-Aware Prefetching Mechanism for Video Streaming”, WWW-2003 [2] He et al, “VOVO: VCR-Oriented Video-On-Demand in Large-Scale Peer-to-Peer Networks”, TPDS-2009 Dislab, NJU CS

  11. System Architecture (2) • Take full advantage of tracker • Tracker has large volume of user viewing logs; • Every node have to contact the tracker to join the system • initiate its neighbor & partner list Dislab, NJU CS

  12. Prediction Approach: Overview • Frequent Sequential Pattern Mining • PerfixSpan[1] : Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. • Splitting Video Segments into Abstract States • Mapping User Logs to Abstract States • Construct Contingency Table (CCT) • Model Utilization [1] Pei et al., “Mining Sequential Patterns by Pattern Growth: The PrefixSpan Approach”, TKDE-2004. Dislab, NJU CS

  13. Prediction Approach (1) 111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111 111111111111111111111111111111111111111111 111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111 Frequent Sequential Patterns Dislab, NJU CS

  14. Prediction Approach (2) • Sequential patterns found may be overlapped? • e.g. <1,2,3,4,5,6,7> and <5,6,7,8,9,10,11,2> • Splitting Approach • Filter out the sub-patterns • e.g. <1,2,3,4>,<1,2,3,4,5>,<1,2,3,4,5,6>,<1,2,3,4,5,6,7> • Scan over the remaining sequential patterns • Cut them into intervals without overlapping - e.g. <1,2,3,4,5,6,7> and <5,6,7,8,9,10,11,2>[1,7],[8,12] • Take intervals not exist in the mined sequential patterns as separate intervals • Split the contiguous intervals into appropriate granularity intervals(States) • - MIN, MAX Dislab, NJU CS

  15. Prediction Approach (3) • Map Raw User logs into State Transitions • <s,s’> • e.g. <1,2,3,4,5,6,7,8,9,10> map to [1,6][7,13] • Transition Table Construction • Simple Frequency Counting Dislab, NJU CS

  16. Data Scheduling • Two stage scheduling strategy: • Stage 1: fetch urgent segments into playback buffer • Guarantee the continuity of normal playback • Urgent line mechanism [1] • Stage 2: prefetch based on prediction • Reduce jump latency • Utilize residual bandwidth [1] Li et al., “ContinuStreaming: Achieving High Plackback Continuity of Gossip-based Peer-to-Peer Streaming”, IPDPS-2008. Dislab, NJU CS

  17. VCR-like Operation Support • The jump process caused by VCR-like operations: • Case 1. The jump segment is already prefetched on the local peer => Just playback!! • Case 2. The jump segment is cached on the partners’ buffer => download and playback! • Case 3. Neither cached on the local peer nor cached by the partners => relocate, connect and download Dislab, NJU CS

  18. Simulation Settings • User Log Generation • Modify GISMO [1] • Using log-normal distribution to let users trend to jump around hot scenes. • The simulation is built on top of a topology of 5000 peer nodes based on the transit-stub model generated by GT-ITM. • The streaming rate is S = 256 Kpbs, the download bandwidth is randomly distributed in [1.5S, 5S]. • The default size of the playback buffer is 30Mbytes, i.e., each peer can cache 120 second recent stream (100 for playback, 20 for prefetching). • The arrival of peers follows the Poisson Process with λ = 5. [1] GISMO: A Generator of Internet Streaming Media Objects and Workloads Dislab, NJU CS

  19. Performance Evaluation (1) Dislab, NJU CS

  20. Performance Evaluation (2) 2 Dislab, NJU CS

  21. Performance Evaluation (3) 3 Dislab, NJU CS

  22. Performance Evaluation 4 Dislab, NJU CS

  23. Conclusions • A practical architecture that can be used in almost all existing P2P VoD systems • A novel and simple prediction approach • State abstraction plays an important role • A two stage data scheduling Dislab, NJU CS

  24. The End Dislab, NJU CS

More Related