Towards classification of SLA based parallel workloads

Traditionally, in High Performance Computing (HPC), a scheduler is a central components of a computing site that aggregates computing resources and is responsible to distribute the incoming load (jobs) between the resources. Under such an environment the optimum performance of the system, against the service level agreement (SLA) based workloads, can be achieved by using integrated heuristic. The SLA defines the service obligations and expectations to use the computational resources. The integrated heuristic is the sum of weighted SLA terms and to find suitable value(s) for weight(s), parameter sweeping is applied in order to obtain the best schedule which is observed to be computationally expensive. It will become more expensive if no value of the computed weights result in improvement in performance with the resulting schedule. Hence, instead of obtaining optimum performance it incurs computation cost. Therefore, there is a need of detection of those situations so that the integrated heuristic can be exploited beneficially. For that reason, a metric is proposed which is based on the concept of utilization, to evaluate the SLA based parallel workloads of independent jobs to detect an impact of integrated heuristic on the workload.