An efficient resource prediction–based scheduling technique for scientific applications in cloud environment

Cloud computing makes scientists to run complex scientific applications. The research community is able to access on-demand compute resources within a short span of time instead of experiencing peak demand bottlenecks. As the demand for cloud resources is dynamic and volatile in nature, this in turn affects the availability of resources during scheduling. In order to allocate sufficient resources for scientific applications with different execution requirements, it is necessary to predict the appropriate set of resources. To attain this objective, a resource prediction–based scheduling technique has been introduced which automates the resource allocation for scientific application in virtualized cloud environment. First, the proposed prediction model is trained on the dataset generated by concurrently deploying tasks of a scientific application on cloud. Then, the resources are scheduled based on the output of proposed prediction technique. The main objective of resource prediction–based scheduling technique is to efficiently handle the resources for virtual machines in order to reduce the execution time, error rate, and improve the accuracy.