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Michael K. Bradshaw, Bing Wang,

Periodic Broadcast and Patching Services - Implementation, Measurement, and Analysis in an Internet Streaming Video Testbed. Michael K. Bradshaw, Bing Wang, Subhabrata Sen , Lixin Gao, Jim Kurose, Prashant Shenoy, and Don Towsley ACM Multimedia 2001. Introduction. Multimedia streaming :

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Michael K. Bradshaw, Bing Wang,

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  1. Periodic Broadcast and Patching Services - Implementation, Measurement, and Analysis in an Internet Streaming Video Testbed Michael K. Bradshaw, Bing Wang, Subhabrata Sen , Lixin Gao, Jim Kurose, Prashant Shenoy, and Don Towsley ACM Multimedia 2001

  2. Introduction • Multimedia streaming : significant loads place on both server and network resources. • Multicast approaches : • Batching • Periodic Broadcast • Patching • Issues : control/signaling overhead, the interaction between disk and CPU scheduling, multicast join/leave times

  3. Batching • Server batches requests that arrive close together in time and multicast the stream to the set of batched clients. • A drawback is that client playback latency increase with an increasing amount of client request aggregation.

  4. Periodic Broadcast • Server divides a video object into multiple segments, and continuously broadcasts segments over a set of multicast addresses. • Earlier portions are broadcast more frequently than later ones to limit playback startup latency. • Clients simultaneously listen to multiple addresses, storing future segments for later playback.

  5. Patching (stream tapping) • Server streams the entire video sequentially to the very first client. • Client-side workahead buffering is used to allow a later-arriving client to receive its future playback data by listening to an existing ongoing transmission of the same video. • Server need only additionally transmit those earlier frames that were missed by the later-arriving client.

  6. Server and Client Architecture

  7. Server Architecture • Server Control Engine (SCE) • One listener thread • A pool of free scheduler threads • One transmission schedule per video • Server Data Engine (SDE) • A global buffer cache manager • Disk thread (DT) : round-lengthδ • Network thread (NT) : round-lengthτ

  8. Schedule Data Structure

  9. Signaling between Server and Client

  10. Testbed (1) • 100 Mbps switched Ethernet LAN • Three machines (server, workload generator and client) with Pentium-II 400 MHz CPU, 400 MB RAM, running Linux OS • Workload Generator generates a background load of client requests in a Poisson manner and logs the timing information for the request to be served

  11. Testbed (2) • Periodic broadcast : • L. Gao, J. Kurose, and D. Towsley.Efficient schemes for broadcasting popular videos (Greedy Disk-conserving Broadcasting segmentation scheme) • l-GDB : the initial segment is lseconds • Subsequent segments are of size 2i-1l where 1 < i < [log2L]

  12. Testbed (3)

  13. Testbed (4) • Patching algorithm : • L. Gao and D. Towsley.Supplying instantaneous video-on-demand services using controlled multicast. (Threshold-based Controlled Multicast scheme) • When client arrival rate for a video is Poisson with parameterλand the length of a video is L seconds, the threshold is chosen to be (sqrt(2Lλ+1)-1)/λ seconds.

  14. Performance Metrics • Server Side : • System Read Load (SRL) • Server Network Throughput (SNT) • Deadline Conformance Percentage (DCP) • Client Side : • Client Frame Interarrival Time (CFIT) • Reception Schedule Latency (RSL)

  15. Catching Implications (1) PB :

  16. Catching Implications (2) Patching :

  17. Catching Implications (3) SRL for patching and 10-GDB with LFU caching

  18. Component Benchmarks

  19. End-End Performance (1) PB : Client Frame Interarrival Time (CFIT) histogram under 3-GDB, 10-GDB, and 30-GDB at 600 requests per minute.

  20. End-End Performance (2) Patching :

  21. Scheduling Among Videos

  22. Conclusions • Network bandwidth, rather than server resources, is likely to be the bottleneck. • PB : 600 requests per minute • Patching : fully loading a 100 Mb network • An initial client startup delay of less than 1.5 sec is sufficient to handle startup signaling and absorb data jitter. • Dramatic reductions can be gained via application-level data caching using LFU replacement policy.

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