A SelfAdaptive Scheduling Algorithm of OnDemand Broadcasts

1
A Self-Adaptive Scheduling Algorithm of
On-Demand Broadcasts
MSWiM 2001 Rome

• 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 waited for
  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
  costend 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 request Q(D,Treq)
• popularity factor of Data at Time PF(D,T)
• safety factor SF(Q,T)(TreqRTL-T) / BP
• Fail rate FR(SF) RR(SF) / R(SF) FR(SF-1)

9
The LDCF Cost Function

• Delay cost for request QDC(Q)
  BPCATCTTFR(SF(Q,T))CF
• Cost function for data D
• Cost(D) SUMQ(D,T)DC(Q)
  PF(D,T)(BPCATCTT)
• SUMQ(D,T) FR(SF(Q,T))CF

10
Experiment settings

• The following parameters are assumed
• M number of data items for broadcast1000
• 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
• CAT1, CTT20, CF2000

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