Flexible approach to schedule tasks in cloud‐computing environments

Cloud computing has changed the traditional large‐scale computational environment by making computing resources available on a pay‐per‐use basis and provides a new direction for network‐based applications by enabling sharing of services. The encouraging of cloud computing is to aggregate heterogeneous distributed sources to solve complicated industrial and scientific issues. The highest concentration of cloud‐computing systems is about sharing resources in a large‐scale multi‐organisational cooperation and their usage in new applications. To achieve this, an efficient scheduling system is a vital part for cloud computing. The dynamic and heterogeneous nature of cloud sources lead to the increased complexity of scheduling algorithms. Therefore, deterministic algorithms may not have enough efficiency to solve this issue. In this study, a new solution is presented to improve dynamic scheduling in cloud environments by combining greedy and max–min scheduling methods. The most important features of the proposed method include reduction of completion of the last task, reduction of total waiting time, observing of load balance and back up of data dynamic operations. The results of the authors’ simulations show performance improvement in comparison with greedy and max–min algorithms.