Scheduling on-demand data broadcast in mixed-type request environments

In many on-demand broadcast environments, there can be a mix of requests, in which some of the requests have real-time constraints, while other requests have no time constraints associated with them. We refer to such environments as mixed-type environments. Existing strategies for on-demand data in broadcast systems typically only consider how to minimize the wait time of the requests, while scheduling strategies for real-time requests typically only consider how to minimize the number of deadlines missed. How to satisfy both of these constraints is a challenging problem whose solution can benefit many applications. In this paper, we present an on-demand broadcast cost model that is more general than existing broadcast cost models because it considers both response time and number of deadlines missed. An analysis of the system is presented as a Markov decision process to ascertain the feasibility of an optimal policy. We propose two scheduling strategies for mixed-type broadcast systems that are based on our cost model: maximum paid cost first and maximum value gained first. The simulation results show that both of our strategies always achieve the best result, when compared to existing broadcast strategies, for varying request arrival rates, real-time to non-real-time request ratios, missed deadline weight values, wait-time bounds on the non-real-time requests, and varying broadcast rates.