Prediction based task scheduling approach for floodplain application in cloud environment

Natural and environmental sciences are one of the scientific domains which seek a lot of attention as it requires high performance computation and large storage space. Cloud computing is such a platform that offers a customizable infrastructure where scientific applications can provision the required resources prior to execution. The elasticity characteristic of cloud computing and it's pay-as-you-go pricing model can reduce the resource usage cost for cloud client's. The various services offered by the cloud providers and the extravagant developments in the domain of cloud computing has attracted many scientists to deploy their applications on cloud. The change in number of tasks of scientific application directly affects the demand of cloud resources. Therefore, to handle the fluctuating demand of resources, there is a need to manage the resources in an efficient manner. This research work focuses on the design of a prediction based scheduling approach which maps the tasks of scientific application with the optimal VM by combining the features of swarm intelligence and multi-criteria decision making approach. The proposed approach improves the accuracy rate, minimizes the execution time, cost and service level agreement violation rate in comparison to existing scheduling heuristics.