Efficient criticality oriented service brokering policy in cloud datacenters

Abstract

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.