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Application-level Prefetching

Application-level Prefetching. CS656 semester project Peixan Li, Jinze Liu, Hexin Wang. Outline. Motivation Solution Implementation Evaluation Test Plan Conclusion. Motivation. Get Better Performance on Remote Data Access over DOS Random Data Access Sequential Data Access

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Application-level Prefetching

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  1. Application-level Prefetching CS656 semester project Peixan Li, Jinze Liu, Hexin Wang

  2. Outline • Motivation • Solution • Implementation • Evaluation • Test Plan • Conclusion

  3. Motivation • Get Better Performance on Remote Data Access over DOS • Random Data Access • Sequential Data Access • e.g., Video-on-Demand Data Transfer • Problems • Low Bandwidth Network • Wait for your data whenever you need it most • General-Purpose OS • Inappropriate Scheduler (RoundRobin) not addressing timing constraints

  4. Solution(I) -- Client Side • Application level Prefetching Cache • Although the fact -- Low Bandwidth Network • Prefetching Cache can reduce data access time • General Data Service • History-based prefetching • Video-on-Demand • Sequential Prefetching

  5. Solution(II) -- Server Side • Admission Control • Avoiding Server Overload • All the admitted tasks can be satisfied using current scheduling algorithm • Server Level Scheduling algorithm • Isochronous Tasks can run first before GP Tasks

  6. Implementation • Client/Server Model • Client Side • Prefetch via Data Compression • Cache Management • Server Side • Admission Control • Scheduling Algorithm

  7. Controller Controller Cache Cache Prefetch Thread Prefetch Thread Client/Server Model Client 1 Client 2 Server Normal Application V/A Player Admission Control Schedule Control Service Thread Service Thread …. File Server Peixian...

  8. Prefetch via Data Compression • Based on data compression techniques • Why is D/C useful for prefetch • Basic law: To represent more common events with short codes and less common events with longer codes • Must be good at recording history and predicting future data • Be particularly good for databases and hypertext systems

  9. History-based Prefetch • We use Ziv-Lempel algorithm • Simple but very good • Predict based on a probabilistic history tree • e.g. “aaaababaabbbab” => (a)(aa)(ab)(aba)(abb)(b) • Sequential prefetch is used when lack of history • Prefetch thread is activated once a request is finished • History tree need to be rebuilt before it becomes too large

  10. Sequential Prefetch • Two kinds of interfaces provided client module • Hread() is for history-based prefetch • Sread() is for sequential prefetch • More kinds of reads can be added, e.g. real-time • When sequential prefetch is used • No history is needed • Only future data need to be cached • Semaphores are used tosynchronized cache-read and cache-write

  11. Cache Management • Cache size dynamically grows and shrinks • With default size and maximum limit • In order to use memory efficiently • In order to provide better performance • Use LRU replacement algorithm • Simple but good enough • No consistency issue since we only have read-only access

  12. Admission Control(I) • Basic Assumption • Isochronous Tasks • Real-time periodic tasks • MPEG-1 requires about 1.5 MbitsPS • MPEG-2 or MPEG-4 requires about 5-10MbitsPS • Require performance guarantee for throughput, bounded latency. • General-Purpose Tasks • Preemptible tasks • Suitable for low-priority background processing

  13. Admission Control(II) • If a new isochronous task is to be admitted • All the previous tasks must be satisfied whenever new task is taken into account or not • The new task can be satisfied under current workload • High frequency tasks run before low frequency tasks • A periodic task can be satisfied means it can finish within each period. • I.e., Real Execution Time <= Period

  14. Admission Control(III) • To admit a new Isochronous Task • I.e. • n -- Total number of isochronous tasks • Ci -- An execution time per period of task i • Ti -- Period of isochronous task i • Disadvantage • General-Purpose Tasks may suffer from starvation

  15. Admission Control(V) • E.g. Task1(C1 = 6, T1 = 10); Task2(C2 = 6, T2 = 20); T3 T2 T1 0 10 20 30 t

  16. Scheduling algorithm • Schedule Algorithm • Isochronous requests scheduled using rate monotonic • The higher frequency, the higher priority • Normal file requests scheduled with round robin • Can be preempted by isochronous tasks Jinze...

  17. Test Plan • Test programs with different access patterns • Sequential remote multimedia access. • Simulated tree-like web document access. • Simulated database access. • Random remote file access. • To test prefetching performance with different test programs. • To test server performance with concurrent requests of different applications

  18. Evaluation • Performance comparison -- Yes/No prefetching • Cache Hit Rate • Received throughput • Server performance with different tasks • Correctness of Admission Control. • Measurement of capacity

  19. Unsolved Problems • Cache cannot be shared between different applications. • Cache data is lost after the termination of application program. • Cache is read-only.

  20. Conclusion • We’ve simulated a client/server model to support application-oriented isochronous prefetching.

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