Scheduling on-demand broadcast with timing constraints

In a distributed system, broadcasting is an efficient way to dispense data in certain highly dynamic environments. While there are several well-known on-line broadcast scheduling strategies that minimize wait time, there has been little research that considers on-demand broadcasting with timing constraints. One application which could benefit from a strategy for on-demand broadcast with timing constraints is a real-time database system. Scheduling strategies are needed in real-time databases that identify which data item to broadcast next in order to minimize missed deadlines. The scheduling decisions required in a real-time broadcast system allow the system to be modeled as a Markov Decision Process (MDP). In this paper, we analyze the MDP model and determine that finding an optimal solution is a hard problem in PSPACE. We propose a scheduling approach, called Aggregated Critical Requests (ACR), which is based on the MDP formulation and present two algorithms based on this approach. ACR is designed for timely delivery of data to clients in order to maximize the reward by minimizing the deadlines missed. Results from trace-driven experiments indicate the ACR approach provides a flexible strategy that can outperform existing strategies under a variety of factors.