A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

Authors

  • Nadim Rana Faculty of Computing Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Johor, Malaysia https://orcid.org/0000-0002-6215-4414
  • Muhammad Shafie Abd Latiff Faculty of Computing Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Johor, Malaysia
  • Shafi'i Muhammad Abdulhamid Federal University of Technology Minna, Nigeria

DOI:

https://doi.org/10.11113/ijic.v8n3.199

Keywords:

Cloud computing, VM scheduling, Metaheuristic, 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.

Author Biography

Nadim Rana, Faculty of Computing Universiti Teknologi Malaysia 81310 UTM Johor Bahru, Johor, Malaysia

Lecturer, Department of Information Systems

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Published

2018-11-21

How to Cite

Rana, N., Abd Latiff, M. S., & Muhammad Abdulhamid, S. (2018). A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm. International Journal of Innovative Computing, 8(3). https://doi.org/10.11113/ijic.v8n3.199

Issue

Section

Computer Science