Task Assignment Based on Prioritising Traffic Flows

We consider the issue of task assignment in a distributed system under heavy-tailed (ie. highly variable) workloads. A new adaptable approach called TAPTF (Task Assignment based on Prioritising Traffic Flows) is proposed, which improves performance under heavy-tailed workloads for certain classes of traffic. TAPTF controls the influx of tasks to each host, enables service differentiation through the use of dual queues and prevents large tasks from unduly delaying small tasks via task migration. Analytical results show that TAPTF performs significantly better than existing approaches, where task sizes are unknown and tasks are non-preemptive (run-to-completion). As system load increases, the scope and the magnitude of the performance gain expands, exhibiting improvements of more than six times in some cases.