Journal of Bionic Engineering

March 2019, Volume 16, Issue 2, pp 354–366| Cite as

Mohammed Joda Usman,Abdul Samad Ismail,Hassan Chizari,Gaddafi Abdul-Salaam,Ali Muhammad Usman,Abdulsalam Yau Gital,Omprakash Kaiwartya,Ahmed Aliyu

1.Department of MathsBauchi State University GadauBauchiNigeria

2.Department of Computer ScienceUniversiti Teknology MalaysiaSkudai JohorMalaysia

3.School of Computing and Technology, Park CampusUniversity of GloucestershireCheltenhamUK

4.Department of Computer ScienceKwame Nkrumah University of Science and TechnologyKumasiGhana

5.Department of Maths and ComputerFederal College of Education Technical GombeGombeNigeria

6.Department of MathsAbubakar Tafawa Balewa University BauchiBauchiNigeria

7.Department of Computer and Information TechnologyNorthumbria UniversityNewcastleUK

Abstract

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability.

Keywords

virtualization green computing cloud datacenter energy optimization 

Full text is available at :

https://link.springer.com/article/10.1007/s42235-019-0030-7