MACHINE LEARNING METHODS FOR REFINING SLA BASED ADMISSION CONTROL AND RESOURCE ALLOCATION IN CLOUD COMPUTING

Abstract

Cloud computing is the most advanced technology in the real world environment and provides flexible and convenient possibilities for users to utilize available services. Resource provisioning to the satisfaction of user requirements becomes the most challenging task in the heterogeneous cloud environment. Proper admission control algorithms need to be proposed for better resource provisioning with improved user satisfaction level. In this research, Knowledge-based Service Level Agreement (SLA) aware admission controlled scheduling and resource allocation are proposed which makes use of machine learning algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN) for better admission control. It seeks to study the knowledge of resource status information by using machine learning algorithms in the training phase. Based on these strategies, admission control would be done in the testing phase which would lead to efficient and better resource provisioning. As our proposed work we mentioned Position Balanced Parallel Particle Swarm Optimization (PBPPSO) is utilized for optimal scheduling and resource allocation which can handle large volumes of tasks in an optimal manner.