A Novel Heuristic Technique for Task Scheduling in Public Clouds

Cloud computing has increased in usage greatly over the last decade. As an increasing number of businesses migrate computation tasks to public clouds, the problem of managing costs of Cloud usage has come to the center of attention in Cloud computing research. Many heuristic techniques have been proposed to allocate tasks to Public Clouds so as to reduce the costs incurred. Among these, Shortest Job First (SJF) and Longest Job First (LJF) are two prominent techniques. In this work, the authors propose a novel heuristic technique that aims to combine SJF and LJF heuristic techniques and improve cost compared to both the techniques. Using Cloudsim, the authors also show the optimum combinations of Amazon Elastic Compute Cloud(EC2) instances where it is most cost effective to schedule tasks.