On-demand Broadcast Scheduling Research Articles

Scaling of On-Demand Broadcast Scheduling in Stressed Networks

The United States is deploying the broadband wireless network for public safety and emergency response. While this will provide a foundational network when disasters or other emergencies appear, the network capacity could still be insufficient when large scale emergency events, such as earthquakes or flooding, happen. On-demand broadcast is a possible solution to address this situation, but its effectiveness remains unknown. In this paper, we aim to uncover the effectiveness of on-demand broadcast at different situations. Specifically, we study the scaling of average response time on the information requests with regard to the indicators of the content diversity–the number of distinct content files and the content popularity distribution. To achieve this goal, we first derive the lower bound of the average response time. We further investigate broadcast for streaming videos and uncover that parallel broadcast could have the same lower bound as the sequential broadcast under certain conditions. Based on the lower bound we derived, we evaluate the scaling of response time with regard to the number of files under different distributions. We further provide numerical results to demonstrate the accuracy of the approximation and the value of the lower bound on evaluating the optimality of a basic heuristic on-demand broadcast scheme.

Profit-based scheduling and channel allocation for multi-item requests in real-time on-demand data broadcast systems

On-demand broadcast is a widely accepted approach for dynamic and scalable wireless information dissemination systems. With the proliferation of real-time applications, minimizing the request deadline miss ratio in scheduling multi-item requests has become an important task in the current architecture. In this paper, we prove the NP-hardness of broadcast scheduling of real-time multi-item requests in both single- and multi-channel environments. Furthermore, we propose two profit-based scheduling algorithms, PVC and SSA, for single- and multi-channel scheduling, respectively, both of which utilize our new concept “profit” of pending items and “opportunity cost” of pending requests. To the best of our knowledge, it is also the first time to introduce opportunity cost, which is derived from economics, into on-demand broadcast scheduling. Based on the scheduling result of PVC for pending requests, SSA is proposed to allocate selected items of scheduled requests to available channels. Finally, simulation results show great improvement in comparison with traditional algorithms. In general, PVC for single channel scheduling is superior to the best of other algorithms in terms of request deadline miss ratio. For multi-channel scheduling, SSA has larger advantage with increasing number of channels in terms of request deadline miss ratio than the best of other algorithms.

Utility driven optimization of real time data broadcast schedules

AbstractData dissemination in wireless environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many environments where the utility of the received data is an equally important criterion as its timeliness. Missing the deadline may reduce the utility of the data but does not necessarily make it zero. In this work, we address the problem of scheduling real time data broadcasts with such soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within an acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby eliminating the requirement of a heuristic in the process. An evolution strategy based stochastic hill-climber is investigated in this context.