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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12996

Title: Virtualization oriented Green Computing in Cloud Datacenter: Flower Pollination Approach
Authors: Joda, Usman Mohammed
Ismail, Abdul Samad
Chizari, Hassan
Abdul-Salaam, Gaddafi
Usman, Ali Muhammad
Gital, Abdulsalam Yau
Kaiwartya, Omprakash
Aliyu, Ahmed
Keywords: Virtualization
Green computing
Cloud
Datacenter
Energy optimization
Issue Date: 2018
Publisher: Journal of Bionic Engineering
Citation: Journal of Bionic Engineering, Vol.15 No.1
Abstract: Cloud computing has observed significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources consuming a high amount of energy and increasing carbon emissions at a hazardous level. In Cloud datacenter, Virtual Machine (VM) need to be allocated on various Physical Machines (PM) 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. FPA is a Natured-Inspired optimization technique used in solving global and numerical optimization problems. A system framework was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy namely, Dynamic Switching Probability (DSP). The framework finds near optimal solution quickly and balances the exploration and exploitation of the global and local search. It considers a processor, storage, and memory constraints of a physical machine while prioritizing energy-oriented allocation for a set of virtual machines. Simulations are performed on MultiRecCloudSim utilizing planet workload. It is evident that the E-FPA outperforms state-of-the-art techniques in terms of energy consumption including 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%. This implies that, the datacenter performance and environmental sustainability has been improved significantly due reduction in energy consumption and as well carbon emission.
Description: This article is published in Journal of Bionic Engineering and also available at https://doi.org/12345-018-1234-x
URI: http://www.springer.com/journal/42235
https://doi.org/12345-018-1234-x
http://hdl.handle.net/123456789/12996
Appears in Collections:College of Science

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