Affiliations: [a] Department of Computer Science & Technology, Gangarampur Govt. Polytechnic, Gangarampur, Dakshin Dinajpur, West Bengal, India | [b] Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani, Nadia, West Bengal, India | [c] Department of Cybersecurity in the School of Applied Computational Sciences (SACS), Meharry Medical College (MMC), Nashville, TN, USA | [d] Department of Computer Science & Engineering, University of Kalyani, Kalyani, Nadia, West Bengal, India
Correspondence:
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Corresponding author: Sourav Banerjee, Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani, Nadia, West Bengal, India. E-mail: [email protected].
Abstract: The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.
Keywords: Cloud data centres, energy consumption, machine learning, urban areas, VM consolidation
