Minimising the execution of unknown Bag-of-Task jobs with deadlines on the Cloud

Abstract

Scheduling jobs with deadlines, each of which de nes the latest time that a job must be completed, can be challenging on the cloud due to incurred costs and unpredictable performance. This problem is further complicated when there is not enough information to e ectively schedule a job such that its deadline is satis ed, 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 delivers 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. It is noted that our proposed algorithm outperforms existing approaches, which use a xed amount of resources by reducing the violation cost by at least two times.