Scheduling for large-scale on-demand data broadcasting

Advances in telecommunications have enabled the deployment of broadcast-based wide-area information services that provide on-demand data access to very 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. In this study we introduce a parameterized algorithm that provides good performance across all of these criteria and can be tuned to emphasize either average or worst case waiting time. Unlike previous work on low overhead scheduling, the algorithm is not based on 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 examine the performance of the algorithm using a simulation model