ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) approach in cloud computing

Abstract

Affordability of appropriate computing resources for satisfying prerequisites of Service Level Agreement (SLA) of clients and optimal utilization of cloud service providers are limited in the present scenario of cloud computing. To overcome these limitations, researchers have exploited various scheduling algorithms to process the deadline based autonomous jobs. The scheduling algorithms however do not support multiprocessor demand and adaptive resource provisioning. This inference triggers to propose a new approach called ScHeduling of jobs and Adaptive Resource Provisioning (SHARP) in cloud computing to handle independent jobs that processes the jobs in a multilevel manner. The SHARP approach embeds multiple criteria decision analysis to preprocess the jobs, multiple attribute job scheduling to prioritize the jobs and adaptive resource provisioning to provide resources dynamically. These contributions alleviate SLA violations in terms of deadline, upgrade client satisfaction and enhance resource utilization. The empirical studies verify the proposed approach in a cloud environment and show the necessity of the proposed approach to support elastic resource provisioning and meet SLA requirements.