Presentation on theme: "1 Analysis of Multimedia Workloads with Implications for Internet Streaming Lei Guo 1, Songqing Chen 2, Zhen Xiao 3, and Xiaodong Zhang 1 Presented by:"— Presentation transcript:
1 Analysis of Multimedia Workloads with Implications for Internet Streaming Lei Guo 1, Songqing Chen 2, Zhen Xiao 3, and Xiaodong Zhang 1 Presented by: Zhen Xiao 1 College of William and Mary 2 George Mason University 3 AT&T Labs – Research The 14 th International World Wide Web Conference
2 Multimedia: Downloading Web Server Web Browser Media Player HTTP file Long start-up latency Potential waste of traffic
3 Multimedia: Pseudo Streaming Web Browser Media Player Web Server HTTP also called progressive downloading or progressive playback
4 Multimedia: Streaming Web Server Web Browser Media Player HTTP meta file Streaming Server RTSP/MMS/HTTP RTP/RTCP
5 Goals and Objectives How is multimedia content delivery doing in practice? –Streaming has many advantages over downloading for multimedia traffic –But what percentage of multimedia traffic is delivered via streaming? What are the implications of different content delivery methods for multimedia traffic? –bandwidth efficiency, playback quality, etc. –Can we quantify the actual benefits of a streaming service? What can we do to improve the current content delivery practice for multimedia traffic?
6 Existing Work Streaming/Web sites –A small number of popular servers –No study on large number of Web sites Clients –Educational [USITS01][NOSSDAV01] or enterprise environments [NOSSDAV02] –Very few study on commercial workloads Data Sources –Pre-stored video objects [MMCN98], server logs [NOSSDAV02] –No flow level information Focuses –Object popularity and sharing patterns, client interactivity [WWW01][ICDCS05] –Few on content delivery methods
7 Our Contributions Analyze two large commercial multimedia workloads –Much larger scale –More detailed information (e.g., byte counters) –Focus on multimedia delivery methods, bandwidth efficiency, playback quality Design and simulation of the AutoStream system –Provide streaming service for standard Web servers –Share the cost of streaming service among content providers
8 Outline Background Trace Collection and Processing Workload Analysis AutoStream Conclusions and future work
9 Trace Collection Two packet level media workloads collected with the Gigascope appliance –Server Workload: a large number of commercial Web sties hosted by a major ISP (a Web server farm) –Client Workload: a large group of home users connected to the Internet via a well-known cable company (cable clients) –The two workloads are independent! –24 hour duration: 06/15/2004 8pm – 06/16/2004 8pm We collected: –The first IP packets of all HTTP requests and responses –The first IP packets of all RTSP and MMS control messages –Byte counters: the number of bytes transferred through each TCP/UDP connection per second –All HTTP based P2P traffic were carefully filtered out
10 Traffic Overview Total data size: 100GB in gzip format Server workload –1,095,984 media requests/response pairs –4,498 unique server IPs, 79,309 unique client IPs Client workload –579,693 media requests/response pairs –13,110 unique server IPs, 7,906 unique client IPs
11 Trace Processing Downloading –User-Agent –User-Agent in HTTP request is a Web browser –Content-Typeaudiovideo –Content-Type in HTTP response is audio or video –application/multipart.mp3.mpeg –application/multipart : based on 34 most popular suffixes for media files (e.g..mp3,.mpeg, etc.) Pseudo streaming –Subtle differences from downloading –User-Agent –User-Agent in HTTP request corresponds to a media player Most streaming uses RTSP and MMS –HTTP based streaming is very small
12 Trace Processing (Contd) Processing –Decoded most popular media formats: Windows, Real, and QuickTime –Extracted URL, media encoding rate, and playback time. Content-Length –Requested traffic: Content-Length in HTTP response or media length and encoding rate extracted from RTSP/MMS messages –Transferred traffic: actually transferred data based on byte counters.
13 Outline Background Trace Collection and Processing Workload Analysis AutoStream Conclusions and future work
17 Early Terminated Connections % Connections% Traffic Compared with downloading, clients using pseudo streaming tend to abort more and early, and hence cause less traffic.
18 Client access duration in streaming Server WorkloadClient Workload 11% 44% 20% 35% Compared with downloading and pseudo streaming, clients using streaming are much more likely to terminate their access to an object earlier.
19 Bandwidth Efficiency Definition: The percentage of requested traffic that was actually transferred.
20 Rate Mismatch in Pseudo Streaming Server WorkloadClient Workload Downloading rate: averaging the transferred bytes over the data transmission time. Streaming rate: average object encoding rate Rate mismatch in pseudo streaming is common, which can cause the client to experience frequent delays in order to refill its buffer.
21 Advantages of Streaming Rate adaptation –Transcoding –Multiple-Bit-Rate Encoding Intelligent Streaming from Microsoft SureStream from Real Networks –Frame thinning Prioritized retransmissions –UDP based streaming Server workload: RTSP: 10.4%, MMS: 23.5% Client workload: RTSP: 26.8%, MMS: 21.5% –Only re-send lost packets that can arrive in time for the playback Support for interactive operations –Pause, fast forward, rewind, etc.
22 Summary of Findings We found Streaming is the most efficient approach for multimedia delivery. but Most multimedia traffic is delivered via downloading. Why is streaming not widely used? Streaming has associated expenses Benefits not obviousAutoStream Share the cost of streaming among content providers Try streaming before you buy
23 Outline Background Trace Collection and Processing Workload Analysis AutoStream Conclusions and future work
24 AutoStream Overview AutoStream Server Farm Client Existing Web servers can provide real streaming service! HTTP RTSP/MMS/HTTP RTP/RTCP Try before you buy! server
25 AutoStream Architecture Client Request Handler Virtual Streaming Engine Windows Media Streaming Engine Prefix Cache Engine Real Media Streaming Engine QuickTime Media Streaming Engine Streaming Media Converter
26 Evaluation Trace driven simulation using the server workload –On-demand cache with initial segments only. Cache cleaned hourly –When converting non-streaming traffic into streaming, assume the same access patterns as existing streaming traffic –When the available bandwidth to a client is lower than the encoding rate of the media, transcode the media into an appropriate lower encoding rate Metrics –Traffic reduction due to changes in access patterns Benefit of prefix caching is not included –Start-up latency reduction for downloading traffic
27 Traffic Reduction AutoStream reduces downloading traffic by 78% and pseudo streaming traffic by 72%. Without transcoding, the reductions are 56% and 43%.
28 Start-up Latency Reduction 63% sessions previously experiencing start up delays no longer do after AutoStream
29 Outline Background Trace Collection and Processing Workload Analysis AutoStream Conclusions and future work
30 Conclusions and Future Work We found that streaming has many benefits for delivering multimedia traffic, but only limited deployment. We proposed AutoStream, a system to bridge the gap between the potential and practice in multimedia delivery. Lesson learned – always do reality check –Dont assume If a technology is good, itll be used. Future work –Multimedia delivery in peer-to-peer systems –Streaming delivery quality on the Internet