Minimising the Execution of Unknown Bag-of-Task Jobs with Deadlines on the Cloud

Abstract

Scheduling jobs with deadlines, each of which defines the latest time that a job must be completed, can be challenging on the cloud due to the incurred costs and unpredictable performance. This problem is further complicated when there is not enough information to effectively schedule a job such that its deadline is satisfied, and the cost is minimised. In this paper, we present an approach to schedule jobs, whose performance are unknown before execution, with deadlines on the cloud. By performing a sampling phase to collect the necessary information about those jobs, our approach is able to deliver the scheduling decision within 10% cost and 16% violation rate when compared to the ideal setting, which has complete knowledge about each of the jobs from the beginning. Finally, our proposed algorithm outperforms existing approaches, which use a fixed amount of resources by reducing the violation cost by at least two times.