Smart City Gnosys

Smart city article details

Title	Design Of An Energy Efficient Dynamic Virtual Machine Consolidation Model For Smart Cities In Urban Areas
ID_Doc	18790
Authors	Biswas N.K.; Banerjee S.; Ghosh U.; Biswas U.
Year	2023
Published	Intelligent Data Analysis, 27, 5
DOI	http://dx.doi.org/10.3233/IDA-220754
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%. © 2023 - IOS Press. All rights reserved.
Author Keywords	Cloud data centres; energy consumption; machine learning; urban areas; VM consolidation

Similar Articles

Id	Similarity	Authors	Title	Published
40721	0.87	Thiam C.; Thiam F.; Mbaye M.	Optimized Energy And Sla-Aware Virtual Machine Placement Strategies In Cloud: Study	5th IEEE International Smart Cities Conference, ISC2 2019 (2019)
58133	0.87	Reddy K.H.K.; Luhach A.K.; Kumar V.V.; Pratihar S.; Kumar D.; Roy D.S.	Towards Energy Efficient Smart City Services: A Software Defined Resource Management Scheme For Data Centers	Sustainable Computing: Informatics and Systems, 35 (2022)
23237	0.869	Dupont, C; Sheikhalishahi, M; Facca, FM; Cretti, S	Energy Efficient Data Centres Within Smart Cities: Iaas And Paas Optimizations	SMART CITY 360, 166 (2016)
4086	0.869	Choudhury S.; Luhach A.K.; Rodrigues J.J.P.C.; AL-Numay M.; Ghosh U.; Sinha Roy D.	A Residual Resource Fitness-Based Genetic Algorithm For A Fog-Level Virtual Machine Placement For Green Smart City Services	Sustainability (Switzerland), 15, 11 (2023)
37076	0.868	Li L.; Dong J.; Zuo D.; Zhao Y.	Minimizing Sla Violation And Power Consumption In Cloud Data Centers Using Host State 3-Order Markov Chain Model	Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 (2019)
647	0.865	Butt A.A.; Khan S.; Ashfaq T.; Javaid S.; Sattar N.A.; Javaid N.	A Cloud And Fog Based Architecture For Energy Management Of Smart City By Using Meta-Heuristic Techniques	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019 (2019)
23348	0.861	Puttaswamy N.G.; Murthy A.N.	Energy Optimization In Smart Networks Using Machine Learning-Driven Fog Computing To Reduce Unnecessary Cloud Data Transmission	Engineering, Technology and Applied Science Research, 15, 3 (2025)
14483	0.859	Chaudhari A.; Bariyar M.; Pandey A.; Kumari S.; Mohanty A.; Mohapatra H.	Cloud-Enabled Smart Energy Management For Sustainable Urban Development: A Review	Integrating Artificial Intelligence Into the Energy Sector (2025)
2482	0.859	Sharma A.; Kumar N.; Diwaker C.; Sharma B.; Baniwal R.; Bhattacharjee S.B.; Rani S.	A Machine Learning-Based Framework For Energy-Efficient Load Balancing In Sustainable Urban Infrastructure And Smart Buildings	International Journal of Sustainable Building Technology and Urban Development, 15, 4 (2024)
22431	0.859	Rajaan R.; Baishya B.K.; Rao T.V.; Pattanaik B.; Tripathi M.A.; Anitha R.	Efficient Usage Of Energy Infrastructure In Smart City Using Machine Learning	EAI Endorsed Transactions on Internet of Things, 10 (2024)