A Novel Adaptive Framework for Wireless Push Systems Based on Distributed Learning Automata
Wireless Personal Communications: An International Journal
Optimizing the stretch of independent tasks on a cluster: From sequential tasks to moldable tasks
Journal of Parallel and Distributed Computing
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On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. We need an on-demand broadcast scheduling algorithm which can balance individual and overall performance, at the same time avoid the starvation of data items, and scale in terms of client population, database size, and data size in heterogeneous settings. As stretch is regarded as a fair performance metric for variable-sized data requests, in this paper, we propose a new preemptive, heuristic online scheduling algorithm, called PRS for on-demand broadcast system to optimize the worst case stretch across all criteria. We have done a series of simulation experiments to evaluate the performance of our algorithm as compared with other recently proposed methods under a range of scenarios. The experimental results show that our algorithm can substantially reduce the maximum stretch without jeopardizing the overall system performance.