A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre
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Abstract (arXiv)
To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and non-dominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2021.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 0; 2024: 1; 2025: 1.
8 papers.
- 2021Credit Card Fraud Detection using Machine Learning: A Studycited by 6
- 2022HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Modelcited by 5
- 2022A Privacy-Preserving Outsourced Data Model in Cloud Environmentcited by 3
- 2021A study on Machine Learning Approaches for Player Performance and Match Results Predictioncited by 2
- 2022MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Daycited by 2
- 2022PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classificationcited by 1
- 2024MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environmentscited by 1
- 2025A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Managementcited by 0
- 2025A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management20 Nov 2025
- 2024MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environments19 Dec 2024
- 2022A Privacy-Preserving Outsourced Data Model in Cloud Environment24 Nov 2022
- 2022PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification21 Mar 2022
- 2022MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day11 Mar 2022
- 2022HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model10 Mar 2022
- 2021A study on Machine Learning Approaches for Player Performance and Match Results Prediction23 Aug 2021
- 2021Credit Card Fraud Detection using Machine Learning: A Study23 Aug 2021