Eand#64259;cient Resource Prediction and Scheduling Approach for Scientiand#64257;c Applications in Cloud Environment

Abstract

Scientiand#64257;c Computing uses the state-of-the-art of high performance computing capabilities to solve the complex problems in various scientiand#64257;c domains such as weather forecasting, earthquake, sub-atomic particle behavior, turbulent and#64258;ows and manufacturing processes etc. As the demand of resource requirements for solving the scientiand#64257;c problems is dy namic, so there is a need for a and#64258;exible platform which can handle the above-mentioned challenges in scientiand#64257;c applications concerning data storage and computation. Cloud computing provides a dynamic environment for deploying scientiand#64257;c applications by oand#64256;ering services such as infrastructure, platform and software. Various other features such as on-demand service, resource pooling, pay-as-per-use, elasticity, etc has attracted the scientists to deploy scientiand#64257;c applications on cloud. For eand#64256;ective utilization of virtualized resources in cloud, there is a need for eand#64259;cient prediction based scheduling of tasks inorder to maximize performance and minimize execution time. Therefore, it is essential to and#64257;rst predict the resource requirements for scientiand#64257;c applications and then schedule them appropriately to meet the Quality of Service (QoS) requirements of the scientiand#64257;c users by taking SLA violations into consideration. To achieve the set objectives, an extensive literature survey of existing scientiand#64257;c applica tions has been done. Furthermore, state-of-the-art prediction techniques and scheduling approaches have been surveyed. From the literature, it can be inferred that prediction based scheduling is a challenging issue which needs to be handled carefully. To address these problems, and#64257;rstly a Regressive Ensemble Approach for Predicting (REAP) resource usage has been proposed and based on the predicted set of resources a scheduling ap proach (RPS) has been devised.