Scheduling Workflow Applications on the Heterogeneous Cloud Resources

As cloud computing model recently become promising and enables users to obtain their required services, many users desirous to run their workflow applications on it. Scheduling workflow is one of the most important challenges in the cloud. For optimal use of the capabilities of the distributed system, an efficient scheduling algorithm is needed. Addressing the problem of scheduling workflow applications onto Cloud environment is the main contribution of this paper. Heterogeneity of resource types is one of the most important issues which significantly affect workflow scheduling in Cloud environment. On the other hand, a workflow application is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea in this paper is to match the heterogeneity in workflow application to the heterogeneity in Cloud environment. To obtain this objective a new scheduling algorithm is introduced, which is based upon the idea of detecting the set of tasks that could run concurrently and distribute them into different sub-workflows and then allocate each sub-workflow in resource cluster instead of allocating individual tasks. This can reduce inter-task communication cost and thus improve workflow execution performance. First we perform global scheduling and then conduct local scheduling. On the Global-scheduling to achieve high parallelism the received DAG partition into multiple sub-workflows that is realized by WPRC algorithm. On the Local-scheduling, sub-workflows were generated at the global level are dispatched to selected resource clusters. We used the simulation to evaluate the performance of the proposed algorithm in comparison with three well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.