Performance analysis of multi-level time sharing task assignment policies on cluster-based systems

There is extensive evidence indicating that modern computer workloads exhibit highly variability in their processing requirements. Under such workloads, traditional task assignment policies do not perform well. Size-based policies perform significantly better than traditional policies under highly variable workloads. The main limitation of existing size-based policies though is that these have been targeted for batch computing systems. In this paper, we provide performance analysis of 3 novel task assignment policies that are based on multi-level time sharing policy, namely MLMS (Multi-level Multi-server Task Assignment Policy), MLMS-M (Multi-level Multi-server Task Assignment Policy with Task Migration) and MLMS-M* (Multitier Multi-level Multi-server Task Assignment policy with Task Migration). These policies attempt to improve the performance first by giving preferential treatment to small tasks and second by reducing the task size variability in host queues. MLMS only reduces the variability of tasks locally, while MLMS-M and MLMS-M* utilise both local and global variance reduction mechanisms. MLMS outperforms existing size-based policies such as TAGS under specific workload conditions. MLMS-M outperforms TAGS under all the scenarios considered. MLMSM* outperforms TAGS and MLMS-M under specific workload conditions and vice versa.