1. A Self-Adaptive Scheduling Algorithm of On-Demand Broadcasts
MSWiM Rome
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)
Good morning ladies and gentlemen. The topics to present here is an algorithm that enhances the on-demand broadcast mobile system. I am not an expert in this area, but I would like to present the works of my colleagues and hope it could still draw your attention to their great work.

2. Presentation On-demand broadcasts Previous studies
New metrics of performance
The LDCF algorithm
Experiments
Conclusion
In this presentation, we shall discuss the problem of on-demand broadcasts and the solution to the drawbacks of the previous methods. In the light of new metrics of performance, the large delay cost first algorithm is presented. The experiments have shown the improvement of the new algorithm over the existing algorithms.

3. A typical on-demand broadcast system
A typical on-demand broadcast mobile system responses the mobile users’ pull requests by broadcasting. The user pull requests are sent to the server by an up-link channel.

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
The difference between on-demand broadcasts and purely pull-based broadcasts lies in three points. First, it needs an uplink channel to send data access requests from the users to the server. Secondly, the server does not know the access profile of the mobile users. Lastly, requests may fail because of time out.

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?
Previous work includes First-come Firsts serve, Most-request First, Long wait first. These work mainly discuss how to reduce the average access time of mobile users; however, they don’t consider how to handle the failure requests 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)
Analyzing the average response time, it is spent over Access time cost, Tuning time cost and the cost of handling failure request. This gives a new metrics of the performance.

7. Largest Delay Cost-First algorithm
Input: a request sequence Output: a broadcast schedule while true do receive new requests; for each delayed data item D, compute the cost; broadcast the items with the largest delay cost; end while
The large delay cost first (LDCF) algorithm treats the request sequence and generates a broadcast schedule to improve the performance. The server endlessly check new requests, then compute the largest cost for the delayed data items to decide which items are to broadcast.
The critical part of the algorithm is to compute or estimate the delay cost.

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)=(Treq+RTL-T) / BP
Fail rate: FR(SF) = RR(SF) / R(SF) * FR(SF-1)
In order to compute the delay cost, several parameters are used.
First, the average costs for access time, tuning time and failure are known constants.
The broadcast period (BP) covers the time for index and data of a request message.
The server sets a response time limit to the messages. The data should be broadcasted at a time earlier than the request time plus the limit.
During the time, the variable parameters according to the varying access request of data D at a request time T request are defined as popularity factor of data at time, the safety factor to evaluate whether a message is urgent. And fail rate is inductively derived from the previous safety factor times the remained request rate.

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}
Given the parameters, the delay cost for a request is accumulated by access time, tuning time and failure process time costs respectively.
The cost function for a data D is thus the sum of the delay cost of requests that involves the data item.

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
In our experiments, we assumed the number of data items is 1000, the number of data items in a broadcast period is 100. The data index length is 6. The zipf(k) defines the the skewness of access distribution. The average cost of accessing time, tuning time and failure handling time are 1, 20 and 2000 respectively.

11. 1. Average Cost when fail rate of request is low
We have done several experiments to show the advantages of LDCF method over LWF, FCFS, MRF schemes.
First experiment shows when fail rate is low (data=100, request number per slot is 10.10, zipf(0) distribution and RTL from 1500 to 3500.
FCFS is the worst and LDCF is slightly better than the MRF and LWF methods.

12. 2. Average Cost when fail rate is high
When the fail rate is high because data is 1020, request number per slot is , and RTL is only One can see that LDCF has the lowest average cost, while LWF is slightly worse.

13. 3. Average Cost vs BP
To compare the best two methods LDCF and LWF more closely, change the BP so that data varies from 60 to 200, the request per slot is and RTL is 1500, one can see that LDCF is uniformly better than LWF.

14. 4. Average Cost vs RTL
The same is true for comparison of different RTL when data item is 100 and request per slot is

15. 5. Average Cost vs skewness of data access distribution
Lastly, the skewness of data access distribution is varied to compare the two methods.
The left chart shows when the zipf(k) value are discrete points and the right chart averages the result for the zipf(k) in a random value in the specified ranges. The LDCF method is better and more stable to change.

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.
The final conclusion is, one should consider not only the access time but also the tuning time and failure handling time in the model. The experiments shows that the LDCF method is superior to the compared LWF, FCFS and MRF methods.
Thank you very much.

17. Thanks MSWiM 2001 Program Committee