A Cost Model for Scheduling On-Demand Data Broadcast in Mixed-Type Request Environments

Abstract

Scheduling strategies for on-demand data in broadcast systems typically consider how to minimize the wait time of the requests. When users' requests for data in a broadcast system have real-time constraints, scheduling strategies for such requests typically only consider how to minimize the number of deadlines missed. There are many applications with both real-time and non-real-time requests that would benefit from a broadcast scheduling strategy that considers both the timing constraints and the wait times of requests. We refer to such a broadcast environment as a mixed-type request broadcast environment. In this paper, we present an on-demand broadcast cost model for mixed-type broadcast environments that considers both the response time and number of deadlines missed. We propose a scheduling strategy for mixed-type broadcast systems, called the maximum paid cost first (MPCF) that is based on this cost model. The simulation results show that our MPCF strategy always achieves the best result for varying request arrival rates, ratio of non-real-time requests and real-time requests, and a weighted missed deadline value, when compared to existing broadcast strategies.

Performance analysis of data scheduling algorithms for multi‐item requests in multi‐channel broadcast environments

“…SIN outperforms other existing real-time algorithms significantly. Lei et al [15] considered a mixed-type request environment where both the timing constraint and the waiting time of requests are taken into consideration. They presented an on-demand broadcast cost model for the mixed-type request environments and upon that, they proposed a scheduling strategy named MPCF (Maximum Paid Cost First).…”