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

Index Introduction Measurement Result Analysis Modeling

Introduction

Measurement Result

Analysis

Modeling

Limited Cache Size Cache: For P2P sharing Live service: like TV Peers keep forwarding to watch the newest scene. Old content become out-of-date and is not required by any peers, so it can be discarded by peers safely So the cache size can be limited 10s-100s

Cache: For P2P sharing

Live service: like TV

Peers keep forwarding to watch the newest scene.

Old content become out-of-date and is not required by any peers, so it can be discarded by peers safely

So the cache size can be limited

10s-100s

Sliding Window Chunks may arrive out of sequence not a FIFO

Chunks may arrive out of sequence

not a FIFO

Example A new chunk is received Assume a fixed size cache 1111111111111…………1111111111 001011011 1111111111111…………1111111111 001011011 1111111111111 0000000000001 Get a new chunk Reject the same amount of chunks No, We are downloading!

A new chunk is received

Assume a fixed size cache

Rate Changes Ideal stable status Cache rejection rate = Chunk arriving rate = Server upload rate Rate Change 8 chunks/s -> 10 chunks/s With fixed size chunk, it reflects the change of encoding rate Should sync, but how sync? Immediately delay

Ideal stable status

Cache rejection rate = Chunk arriving rate = Server upload rate

Rate Change

8 chunks/s -> 10 chunks/s

With fixed size chunk, it reflects the change of encoding rate

Should sync, but how sync?

Immediately

delay

Measurement Design our PPLive crawler to actively crawl the buffer status of media server and peers 1st May, 2007, 4hr. 1 PPLive channel Media Server {head chunk id, end chunk id} crawl interval: 10s 376 Peers: {head chunk id, bitmap} crawl interval: 5s

Design our PPLive crawler to actively crawl the buffer status of media server and peers

1st May, 2007, 4hr.

1 PPLive channel

Media Server

{head chunk id, end chunk id}

crawl interval: 10s

376 Peers:

{head chunk id, bitmap}

crawl interval: 5s

Rs vs. Ro(One Peer) Rate Change Tracking (PLL) Latency

Peers Change Ro at Different Time 大多分布在 50s-100s 之间，极个别有 300s 延时

大多分布在 50s-100s 之间，极个别有 300s 延时

变化点比较集中。进一步分析数字特征 At the Same Chunk!

变化点比较集中。进一步分析数字特征

Detail, 4 Peers, Rate Change Point 4

Rs also Change at the Same Chunk! 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

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

Behavior & Meaning Peer’s cache rejection synchronizes with media server’s chunk upload on chunk What’s its underlying meaning? Fixed Time Rejection Algorithm 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 Inspiration: Time is the most important property in P2P Live streaming system It is invariable in the universe Indirect, looks good

Peer’s cache rejection synchronizes with media server’s chunk upload on chunk

What’s its underlying meaning?

Fixed Time Rejection Algorithm

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

Inspiration:

Time is the most important property in P2P Live streaming system

It is invariable in the universe

Indirect, looks good

Modeling Virtual Buffer Characteristic FIFO Buffer Input: Media Server Output: Peer buffer head Fixed duration buffer

Virtual Buffer

Characteristic

FIFO Buffer

Input: Media Server

Output: Peer buffer head

Fixed duration buffer

Validation

Virtual Buffer Abstract The buffer abstract includes the P2P network The chunk propagation process in the P2P network can be modeled with this abstraction Sliding windows model Key: It is measurable! It can be validated in the real world PPLive network

The buffer abstract includes the P2P network

The chunk propagation process in the P2P network can be modeled with this abstraction

Sliding windows model

Key: It is measurable!

It can be validated in the real world PPLive network

Thanks!

Backup

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

Comparison: Ro and Rs Change Change Chunk Offset Difference -126 56 186 -42 -72 3 Difference 6 5 4 3 2 1 Interval

Lack of Explicit Result Misc existed system report Coolstreaming, Anysee, GridMedia, etc. 10s-200s Measurement: 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 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

Misc existed system report

Coolstreaming, Anysee, GridMedia, etc.

10s-200s

Measurement:

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

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

Performance Stable sharing for partner peers Avoid the abrupt rejection problem Adaptively adjusts buffer size according to the streaming rate Smoothly change buffer size when chunk rate change

Stable sharing for partner peers

Avoid the abrupt rejection problem

Adaptively adjusts buffer size according to the streaming rate

Smoothly change buffer size when chunk rate change

Reference Y. Zhou, D. M. Chiu, and John C.S Lui, "A Simple Model for Analyzing P2P Streaming Protocols", The fifteenth IEEE InternationalConference on Network Protocols (ICNP 2007), Bei Jing, China, Oct. 2007 Our paper: Measure and Model P2P Streaming System by Buffer Bitmap, To appear in HPCC 2008.

Y. Zhou, D. M. Chiu, and John C.S Lui, "A Simple Model for Analyzing P2P Streaming Protocols", The fifteenth IEEE InternationalConference on Network Protocols (ICNP 2007), Bei Jing, China, Oct. 2007

Our paper: Measure and Model P2P Streaming System by Buffer Bitmap, To appear in HPCC 2008.