A Measurement Study of Cache Rejection in P2P Live Streaming System

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  • A Measurement Study of Cache Rejection in P2P Live Streaming System

    1. 1. A Measurement Study of Cache Rejection in P2P Live Streaming System Yishuai Chen*, Changjia Chen, Chunxi Li Network Research Group, Telecom Lab, Beijing Jiaotong University http:// telcomlab.googlepages.com
    2. 2. Index <ul><li>Introduction </li></ul><ul><li>Measurement Result </li></ul><ul><li>Analysis </li></ul><ul><li>Modeling </li></ul>
    3. 3. Limited Cache Size <ul><li>Cache: For P2P sharing </li></ul><ul><li>Live service: like TV </li></ul><ul><ul><li>Peers keep forwarding to watch the newest scene. </li></ul></ul><ul><ul><li>Old content become out-of-date and is not required by any peers, so it can be discarded by peers safely </li></ul></ul><ul><li>So the cache size can be limited </li></ul><ul><ul><li>10s-100s </li></ul></ul>
    4. 4. Sliding Window <ul><li>Chunks may arrive out of sequence </li></ul><ul><li>not a FIFO </li></ul>
    5. 5. Example <ul><ul><li>A new chunk is received </li></ul></ul><ul><ul><li>Assume a fixed size cache </li></ul></ul>1111111111111…………1111111111 001011011 1111111111111…………1111111111 001011011 1111111111111 0000000000001 Get a new chunk Reject the same amount of chunks No, We are downloading!
    6. 6. Rate Changes <ul><li>Ideal stable status </li></ul><ul><ul><li>Cache rejection rate = Chunk arriving rate = Server upload rate </li></ul></ul><ul><li>Rate Change </li></ul><ul><ul><li>8 chunks/s -> 10 chunks/s </li></ul></ul><ul><ul><li>With fixed size chunk, it reflects the change of encoding rate </li></ul></ul><ul><ul><li>Should sync, but how sync? </li></ul></ul><ul><ul><ul><li>Immediately </li></ul></ul></ul><ul><ul><ul><li>delay </li></ul></ul></ul>
    7. 7. Measurement <ul><li>Design our PPLive crawler to actively crawl the buffer status of media server and peers </li></ul><ul><li>1st May, 2007, 4hr. </li></ul><ul><ul><li>1 PPLive channel </li></ul></ul><ul><ul><li>Media Server </li></ul></ul><ul><ul><ul><li>{head chunk id, end chunk id} </li></ul></ul></ul><ul><ul><ul><li>crawl interval: 10s </li></ul></ul></ul><ul><ul><li>376 Peers: </li></ul></ul><ul><ul><ul><li>{head chunk id, bitmap} </li></ul></ul></ul><ul><ul><ul><li>crawl interval: 5s </li></ul></ul></ul>
    8. 8. Rs vs. Ro(One Peer) Rate Change Tracking (PLL) Latency
    9. 9. Peers Change Ro at Different Time <ul><li>大多分布在 50s-100s 之间,极个别有 300s 延时 </li></ul>
    10. 10. <ul><li>变化点比较集中。进一步分析数字特征 </li></ul>At the Same Chunk!
    11. 11. Detail, 4 Peers, Rate Change Point 4
    12. 12. Rs also Change at the Same Chunk! <ul><li>因为 Rs 的取样间隔长,所以最后获得的 Rs 变化曲线比较平缓,通常需要 1.5-2K offset 范围来完成速率改变 </li></ul>
    13. 13. Behavior & Meaning <ul><li>Peer’s cache rejection synchronizes with media server’s chunk upload on chunk </li></ul><ul><li>What’s its underlying meaning? </li></ul><ul><ul><li>Fixed Time Rejection Algorithm </li></ul></ul><ul><ul><ul><li>No matter how chunk rate changes, server always upload 1s’ content in 1s, peers playback 1s’ content in 1s, therefore, it is natural to reject 1s’ content in 1s </li></ul></ul></ul><ul><li>Inspiration: </li></ul><ul><ul><li>Time is the most important property in P2P Live streaming system </li></ul></ul><ul><ul><ul><li>It is invariable in the universe </li></ul></ul></ul><ul><li>Indirect, looks good </li></ul>
    14. 14. Modeling <ul><li>Virtual Buffer </li></ul><ul><li>Characteristic </li></ul><ul><ul><li>FIFO Buffer </li></ul></ul><ul><ul><ul><li>Input: Media Server </li></ul></ul></ul><ul><ul><ul><li>Output: Peer buffer head </li></ul></ul></ul><ul><ul><li>Fixed duration buffer </li></ul></ul>
    15. 15. Validation
    16. 16. Virtual Buffer Abstract <ul><li>The buffer abstract includes the P2P network </li></ul><ul><ul><ul><li>The chunk propagation process in the P2P network can be modeled with this abstraction </li></ul></ul></ul><ul><ul><li>Sliding windows model </li></ul></ul><ul><ul><li>Key: It is measurable! </li></ul></ul><ul><ul><ul><li>It can be validated in the real world PPLive network </li></ul></ul></ul>
    17. 17. Thanks!
    18. 18. Backup
    19. 19. Numerical Result Rate change chunk offset: 104923 94901 55382 53509 33106 24206 Max 104890 94879 55372 53474 33086 24193 Min 33 22 10 35 20 13 Max-Min 104909 94895 55377 53498 33097 24200 Mean 12.59 7.17 4.84 11.14 7.20 4.93 Std. Dev 6 5 4 3 2 1 Interval
    20. 20. Comparison: Ro and Rs Change Change Chunk Offset Difference -126 56 186 -42 -72 3 Difference 6 5 4 3 2 1 Interval
    21. 21. Lack of Explicit Result <ul><li>Misc existed system report </li></ul><ul><ul><li>Coolstreaming, Anysee, GridMedia, etc. </li></ul></ul><ul><ul><li>10s-200s </li></ul></ul><ul><li>Measurement: </li></ul><ul><ul><li>PPLive: “adaptively allocated buffer size according to the streaming rate and the buffering time period specified by the media source” [X. Hei, C. Liang, J. Liang, Y. Liu and K. W. Ross, “A Measurement Study of a Large Scale P2P IPTV System”, Nov 2006 </li></ul></ul><ul><ul><li>Method: downloading media file from its local streaming server after physically disconnecting the PC from network. Found buffer size varied from 7.8 MBytes to 17.1Mbytes </li></ul></ul>
    22. 22. Performance <ul><li>Stable sharing for partner peers </li></ul><ul><ul><li>Avoid the abrupt rejection problem </li></ul></ul><ul><li>Adaptively adjusts buffer size according to the streaming rate </li></ul><ul><ul><li>Smoothly change buffer size when chunk rate change </li></ul></ul>
    23. 23. Reference <ul><ul><li>Y. Zhou, D. M. Chiu, and John C.S Lui, &quot;A Simple Model for Analyzing P2P Streaming Protocols&quot;, The fifteenth IEEE InternationalConference on Network Protocols (ICNP 2007), Bei Jing, China, Oct. 2007 </li></ul></ul><ul><ul><li>Our paper: Measure and Model P2P Streaming System by Buffer Bitmap, To appear in HPCC 2008. </li></ul></ul>

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