We consider a distributed server system and ask which policy should be used for assigning jobs (tasks) to hosts. In our server, jobs are not preemptible. Also, the job's service demand is not known a priori. We are particularly concerned with the case where the workload is heavy-tailed, as is characteristic of many empirically measured computer workloads. We analyze several natural task assignment policies and propose a new one TAGS (Task Assignment based on Guessing Size). The TAGS algorithm is counterintuitive in many respects, including load unbalancing, non-workconserving, and fairness. We find that under heavy-tailed workloads, TAGS can outperform all task assignment policies known to us by several orders of magnitude with respect to both mean response time and mean slowdown, provided the system load is not too high. We also introduce a new practical performance metric for distributed servers called server expansion. Under the server expansion metric, TAGS significantly outperforms all other task assignment policies, regardless of system load.
In REF , Harchol-Balter evaluated on-line job assignment strategies in a distributed server system, where the workload was heavy-tailed and job size was unknown to the scheduler.
Task assignment with unknown duration
JACM
Computer Science