A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud en...

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Bibliographic Details
Published in:IEEE access Vol. 11; pp. 107480 - 107495
Main Authors: Sharma, Neeraj Kumar, Bojjagani, Sriramulu, Reddy, Y. C. A. Padmanabha, Vivekanandan, Manojkumar, Srinivasan, Jagadeesan, Maurya, Anup Kumar
Format: Journal Article
Language:English
Published: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
branch-and-price
Cloud computing
Computer centers
data center
Data centers
Energy consumption
Energy efficiency
Energy management
energy-aware
ESV
physical machine
Resource management
Resource utilization
SLA
Virtual environments
virtual machine
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Summary:Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3320729