Allocating Soft Real-Time Tasks on Cluster

Abstract

We consider distributing soft real-time tasks on a cluster of multiple homogeneous servers. The question considered here is how to assign incoming soft real-time tasks to these servers for better performance, measured by the fraction of tasks that miss their deadlines. In this paper, two architectures are taken into account-centralized and distributed. Within the distributed architecture, four dispatching policies-round robin, Bernoulli splitting, joining the shortest queue, and chopping-are analyzed and evaluated under the same condition. In the analysis, an approximate method is proposed and evaluated for the joining the shortest queue policy. The results show that for the distributed architecture, the joining the shortest queue policy performs the best. The chopping policy previously proposed has its limitation and when workload exceeds a moderate level, it performs worse than round robin. In addition, we investigated the impact of using the earliest deadline first to schedule tasks assigned to the same server, and found it can further improve performance.