A cloud-based conceptual framework for multi-objective virtual machine scheduling using whale optimization algorithm

Abstract

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions.