1 / 17

A Self-Adaptive Scheduling Algorithm of On-Demand Broadcasts

This study introduces the LDCF algorithm for on-demand broadcasts, focusing on access time, tuning time, and failure request handling costs. Experiment results show the algorithm's effectiveness over previous approaches. Considered metrics include response time limit, popularity factors, and safety factors. Results compare average costs based on request failure rates, broadcast periods, response time limits, and data access skewness. The study emphasizes the importance of considering various factors in evaluating scheduling algorithms.

bhebert
Download Presentation

A Self-Adaptive Scheduling Algorithm of On-Demand Broadcasts

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MSWiM2001Rome A Self-Adaptive Scheduling Algorithm of On-Demand Broadcasts W. Sun, W. Shi, B. Shi, W. Ji and Y. Yu Department of Compute Science, Fudan University, Shanghai, China Presented by: Yijun Yu (now in Ghent University, Belgium)

  2. Presentation • On-demand broadcasts • Previous studies • New metrics of performance • The LDCF algorithm • Experiments • Conclusion

  3. A typical on-demand broadcast system

  4. Characteristics of an On-Demand Broadcast System Versus a pull-based broadcast system: • Uplink channel is necessary for sending requests from users to the server • The server would not know the access profiles of mobile users • Time out requests should be considered

  5. Previous work First-Come-First-Serve ( FCFS ) Most-Request-First ( MRF ) Long- Wait-First ( LWF ) How to … • Reduce the average access time of mobile users? • Handle a failure request that have waitedfor “quite” a long time?

  6. New metrics of performance The average costs composed of • Access Time cost ( CAT ) • Tuning Time cost ( CTT ) • Failure request handling cost (CF)

  7. Largest Delay Cost-First algorithm Input: a request sequenceOutput: a broadcast schedulewhile true do receive new requests; for each delayed data item D, compute the cost; broadcast the items with the largest delay cost;end while

  8. The LDCF Parameters Constants • Average costs: CAT, CTT, CF • broadcast period: BP= index + data • response time limit RTL: T1 T0 + RTL Variables for access requestQ(D,Treq) • popularity factor of Data at Time: PF(D,T) • safety factor: SF(Q,T)=(Treq+RTL-T) / BP • Fail rate: FR(SF) = RR(SF) / R(SF) * FR(SF-1)

  9. The LDCF Cost Function Delay cost for request Q:DC(Q) = BP*CAT+CTT+FR(SF(Q,T))*CF Cost function for data D: Cost(D) = SUMQ(D,T){DC(Q)} = PF(D,T)*(BP*CAT+CTT) +SUMQ(D,T) {FR(SF(Q,T))*CF}

  10. Experiment settings The following parameters are assumed: • M: number of data items for broadcast=1000 • Data: number of data items in one BP unit • Index: length of index = 6 • Received request number per time slot • Zipf(k): skewness of the access distribution • RTL: Response time Limit • CAT=1, CTT=20, CF=2000

  11. 1. Average Cost when fail rate of request is low

  12. 2. Average Cost when fail rate is high

  13. 3. Average Cost vs BP

  14. 4. Average Cost vs RTL

  15. 5. Average Cost vs skewness of data access distribution

  16. Conclusion • When discussing the performance of a scheduling algorithm, we should take into account not only AT, but also TT and request failure. • LDCF was compared with LWF, FCFS and MRF via several experiments, indicating the average cost of LDCF scheduling is the least.

  17. Thanks MSWiM 2001 Program Committee

More Related