A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.