Delay-impact-based local deadline assignment for online scheduling of distributed soft real-time applications

Distributed soft real-time applications often involve multiple jobs that are executed on different processing units. Hence, resource competitions among these applications can be on any processing unit in the system. However, due to distributed nature of these applications, each processing unit may not have the knowledge about the workload on other processing units. Therefore, scheduling decisions made by individual processing units about their local job execution orders may not be optimal for the applications to which the jobs belong with respect to meeting the applications´ end-to-end deadlines. In this paper, we first introduce a metric to measure, at a local processing unit, the risk of a distributed soft real-time application missing its end-to-end deadline. Second, based on the metric, we develop a local deadline assignment algorithm, i.e., the delay-impact-based (DIB) local deadline assignment algorithm. With the DIB algorithm, distributed processing units can independently schedule their local job sets based on the assigned job deadlines with maximized successful ratio of meeting distributed real-time applications´ end-to-end deadlines. We empirically compare the DIB algorithm with three commonly used local deadline assignment algorithms, i.e., the OLDA, Pure, and Norm algorithms. The experimental results show that the DIB algorithm has clear advantage over the OLDA, Pure, and Norm approaches - it results in up to 50%, 35%, and 35% higher successful ratio than the OLDA, Pure, and Norm approaches with respect to meeting application´s end-to-end deadlines, respectively. Furthermore, for those applications that do miss their end-to-end deadlines, the application execution delay ratio resulted by the DIB algorithm is also up to 300%, 50%, and 150% smaller comparing to the other three approaches.