On the performance of real-time multi-item request scheduling in data broadcast environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic data access patterns. Previous studies on time-critical on-demand data broadcast were conducted under the assumption that each client requests only one data item at a time. With the rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has been done. In this paper, we study the behavior of six representative single-item request based scheduling algorithms in time-critical multi-item request environments. The results show that the performance of all algorithms deteriorates when dealing with multi-item requests. We observe that data popularity, which is an effective factor to save bandwidth and improve performance in scheduling single-item requests, becomes a hindrance to performance in multi-item request environments. Most multi-item requests scheduled by these algorithms suffer from a starvation problem, which is the root of performance deterioration. Based on our analysis, a novel algorithm that considers both request popularity and request timing requirement is proposed. The performance results of our simulation study show that the proposed algorithm is superior to other classical algorithms under a variety of