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Integrated Error Management in MoD Services

This paper presents an integrated error management approach to enhance the performance of Media-on-Demand (MoD) servers. It focuses on improving multimedia storage, retrieval efficiency, and optimizing resource usage. By combining traditional error management techniques with advanced coding schemes, the study aims to reduce bottlenecks caused by memory management and communication protocols. Our findings indicate that implementing these integrated strategies increases server capacity, supports more clients, and ensures effective data recovery, leading to a more robust MoD system.

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Integrated Error Management in MoD Services

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  1. Integrated Error Management in MoD Services Pål Halvorsen, Thomas Plagemann, and Vera Goebel University of Oslo, UniK- Center for Technology at Kjeller Norway

  2. Overview • Application Scenario • Integrated Error Management • Basic idea • Code requirements • Possible solutions • Evaluation • Conclusions

  3. Application Scenario Media-on-Demand server: Applicable in applications like News- or Video-on-Demand provided by city-wide cable or pay-per-view companies Multimedia Storage Server Network Network • Retrieval is the bottleneck:Some important factors: • Memory management • Communication protocol processing • Error management • Project goals:Optimize performance within a single server: • Reduce resource requirements • Maximize number of clients

  4. FEC Decoder FEC Encoder Traditional Error Management

  5. FEC Decoder FEC Encoder Integrated Error Management

  6. Correcting Code Requirements • Erasure (known errors) and error (unknown errors) correcting • Decoding throughput (recovery performance) • Amount of redundant data • Applicable within a single file • Cost of decoding function dependent on the corruption rate

  7. Possible Schemes • Error and erasure correcting codes • Bad performance (< 1.7 Mbps) • Not dependent of corruption rate (< 3.5 Mbps without any corruption) • Erasure correcting codes • Adequate performance (> 6 Mbps) • Corrects only erasures • Combined schemes • Erasure correcting / Error correcting • Capable of recovering from most errors • Even more redundant data • Performance still a problem (< 1.7 Mbps) • Erasure correcting / Error detection • Adequate performance (> 6 Mbps) • Capable of recovering from most errors • (Almost) no additional redundant data

  8. Prototype Design • Combination of the traditional UDP checksum and an erasure correcting code (Cauchy-based Reed-Solomon Erasure) • Disk array with 8 disks, i.e., 7 information disks and 1 parity disk: • Write operations a la RAID level 4/5 • Read operations a la RAID level 0 • One codeword, contained in one or more stripes, is read as one read operation • Each striping unit consists multiple symbols each transmitted as a UDP packet

  9. Codeword Size and Amount of Redundancy Requirements: • Codeword_size  n  stripe_size, n  Z • Recover from one disk failure and (most) network errors  Our current approach: • Using a codeword of 256 symbols with 32 redundant symbols  corrects one out of 8 disks  corrects most errors in the network according to our error model, i.e., any 32 out of 256 packets

  10. Correcting codethroughput suggests a largesymbol size Packet (Symbol) Sizes - I Lowthroughput

  11. Packet (Symbol) Sizes - II • Correcting code (startup) delay suggests a small symbol size Lowthroughput Highstartup delay

  12. Results and Observations – IExperimental Setup • Assuming coding performance is only bottleneck • Transmitted a 225 MB file(corresponding to a 5 minutes 6 Mbps playout) • Used a worst-case loss scenario • Used several different machines

  13. Results and Observations – IIServer Side • No extra disk space needed • No additional time needed to retrieve parity data • No overhead managing retransmissions • Increased buffer space and bandwidth requirement of 12.5% storing and transmitting redundant data. • Encoding throughput limitation of ~3 Mbps (Intel, 166 MHz) to ~25 Mbps (Intel, 933 MHz) is eliminated by retrieving parity data from disk •  The server can support more concurrent users

  14. Results and Observations – III Client Side • Increased buffer requirement for decoding the received data (2 MB, 3 MB, 5 MB, and 9 MB using 1 KB, 2 KB, 4 KB, and 8 KB packets, respectively) • Additional CPU requirements recovering lost/corrupted data • Additional (worst case) startup delay between ~0.1 s (Intel, 933 MHz) and ~4.5 s (Intel, 166 MHz) decoding the first block can be experienced • In-time decoding •  hiccup free presentation

  15. Conclusion • The INSTANCE project aims at optimizing the data retrieval in an MoD server • Integrated Error Management • Future work • More information: www.unik.no/~paalh/instance

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