Optimal selection of VMs for resource task scheduling in geographically distributed clouds using fuzzy c‐mean and MOLP

Abstract

Because ofwidespread distribution of resources in the geographically distributed cloud environment, optimal selection of virtual machines (VMs) is one of the most important challenges for the structure of the network. This is due to the high number of data centers and VMs with different qualities of service parameters. Because of redundancy in the VMs and the high number of service parameters, optimal selection of VMs is an NP-hard problem. Therefore, a method is required, which can suggest the best VMs on the basis of the user's request and on the service-level agreements (SLAs). This study focuses on four important factors in SLAs: cost, response time, availability, and reliability. In this paper, we propose a four-tier structure, Observe, Orient, Decide, and Act, where (1) Observe is responsible for continuous monitoring users' requests and characteristics of data centers and VMs, (2) Orient is responsible for clustering data centers using fuzzy c-means and based of the four quality of services (SLA's factors) and then the selection of the most suitable data center cluster for the VMselection, (3) Decide is responsible for making decision on themost suitableVMsusing multiobjective linear programming, and (4)Act is responsible for the execution of the decision. The proposed structure was implemented, and its effectiveness was evaluated through considering the number of SLA violations.