Statistical-Based Heuristic for Tasks Scheduling in Cloud Computing Environment

Abstract

Cloud computing is an emerging and innovative technology that has taken the business information systems to wider extent with the fast sharing of vast web resources over the internet. It considers as an extension to distributed and parallel computing. Additionally, it enables sharing, organizing and aggregation of computational machines to satisfy the user demands. Utilizing the resources efficiently is the main challenge of cloud service provider. Task scheduling in cloud computing plays the main role in decreasing the execution time and cost and hence, increasing the profit. This paper addresses the problem independent tasks scheduling over different virtual machines in computational cloud environment. It introduces two batch mode heuristics algorithms for scheduling independent task: high mean absolute deviation first heuristic and QoS Guided Sufferage-HMADF heuristic. Besides, the paper presented other existing batch mode heuristics such as, Min-Min, Max-Min and Sufferage. The four heuristic modes are simulated and the experimental results are discussed using two performance measures, makespan and machine resource utilization.