Time-Critical On-Demand Broadcast: Algorithms, Analysis, and Performance Evaluation

On-demand broadcast is an effective data dissemination technique to enhance system scalability and deal with dynamic user profiles. In many situations, there is a need for servers to support timely data dissemination. This paper studies online scheduling algorithms for time-critical on-demand broadcast. We propose a novel scheduling algorithm called SIN-α that incorporates the urgency and the number of pending requests. An efficient implementation of SIN-α is presented. Moreover, we analyze the optimal broadcast schedule in terms of request drop rate when the request arrival rate rises towards infinity. Trace-driven experiments demonstrate that SIN-α significantly outperforms existing algorithms over a wide range of workloads. The results also show that the performance of SIN-α approaches the analytical optimum when the request rate is high.