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On-Demand Broadcast Scheduling

dc.contributor.authorAksoy, Demeten_US
dc.contributor.authorFranklin, Michaelen_US
dc.description.abstractBroadcast is becoming an increasingly attractive data dissemination method for large client populations. In order to effectively utilize a broadcast medium for such a service, it is necessary to have efficient, on-line scheduling algorithms that can balance individual and overall performance, and can scale in terms of data set sizes, client populations, and broadcast bandwidth. We propose an algorithm, called RxW, that provides good performance across all of these criteria and that can be tuned to trade off average and worst case waiting time. Unlike previous work on low overhead scheduling, the algorithm does not use estimates of the access probabilities of items, but rather, it makes scheduling decisions based on the current queue state, allowing it to easily adapt to changes in the intensity and distribution of the workload. We demonstrate the performance advantages of the algorithm under a range of scenarios using a simulation model and present analytical results that describe the intrinsic behavior of the algorithm. (Also cross-referenced as UMIACS-TR-98-88)en_US
dc.format.extent453527 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3859en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-98-88en_US
dc.titleOn-Demand Broadcast Schedulingen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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