Workload aware autonomic resource management scheme using grey wolf optimization in cloud environment

Abstract Autonomic resource management on cloud is a challenging task because of its huge heterogeneous and distributed environment. There are several service providers in the cloud to provide a different set of cloud services. These services are delivered to the clients through a cloud network, and it needs to satisfy the Quality‐of‐Service (QoS) requirements of users without affecting the Service Level Agreements. It can only manage through autonomic cloud resource managing frameworks. However, most of the existing frameworks are not much efficient for managing cloud resources because of the varied applications and environments of the cloud. To defeat such problems, this paper proposed the workload aware Autonomic Resource Management Scheme (WARMS) in the cloud environment. Initially, the clustering of cloud workloads is achieved by Modified Density Peak Clustering algorithm. Further, the workload scheduling process is done using fuzzy logic for cloud resource availability. The autonomic system uses Grey Wolf Optimization for virtual machine deployment to achieve optimal resource provisioning. The WARMS system focused on reducing the Service Level Agreement violation, cost, energy usage, and time, and providing better QoS. The simulation results of WARMS shows the system delivering the cloud services more efficiently by the minimized rate of violation and enhanced QoS