Multi-level Multi-server Task Assignment with Work-Conserving Migration

Abstract

Size-based task assignment policies have shown significant performance improvements over traditional task assignment policies under highly variable workload conditions. However, these policies are not suitable to assign tasks in time sharing systems. Moreover, these policies are not scalable and they also generate significant amount of wasted processing. This paper proposes a Multi-Level-Multi-Server Task Assignment Policy with Work-Conserving Migration (MLMS-WC-M) that addresses these issues. MLMS-WC-M has three important features. First, it gives preferential treatment to tasks with short processing requirements. Second, it utilises a 2-level variance reduction mechanism. Third, it supports work-conserving migration. We evaluate the performance of MLMS-WC-M against the performance of several well known task assignment policies. The proposed policy outperforms existing policies significantly under a wide range of workload conditions.