Abstract
The consolidation of virtual machines (VMs) in cloud data centres is an intriguing research subject. The fundamental issue with VM consolidation solutions is the trade-off between energy efficiency, Quality of Service (QoS) efficiency, and optimum Service Level Agreement Violations (SLAV). This study proposes Multiple Resources Aware VM Consolidation (MRA-VC), a unique approach for the dynamic VM consolidation framework in the cloud data centre. The MRA-VC is made up of two contributions: multi-resource-based overload and underload judgment algorithms and a VM placement technique based on Particle Swarm Optimization (PSO). For accurate decision-making on the overloaded and underloaded hosts, multi-resource overload detection algorithms (MR-ODA) and MR-based Underloaded Detection Algorithm (MR-UDA) are designed. The parameters such as CPU utilization, RAM, bandwidth, network traffic, and storage capabilities are computed for MR-ODA and MR-UDA. In MR-UDA, we categorize each underloaded host into either of three states: severe load (SL), moderate load (ML), or low load (LL) using the integrated resource utilization score. This process easily facilitates the subsequent processes of VM migration, VM selection, and placement. The PSO is used to modify the VM selection and placement process in the MRA-VC architecture. PSO-based VM selection and placement methods expand the MR-ODA and MR-UDA algorithms. Optimized VM selection and placement have a direct impact on QoS and energy consumption performance. As a result, after deciding when to migrate, we used the PSO-based optimum VM selection and placement technique. The MRA-VC framework was developed and assessed using the CloudSim tool in terms of energy efficiency and other QoS criteria in comparison to contemporary state-of-the-art approaches.
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The datasets analysed during the current study are available from the corresponding author on reasonable request.
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Songara, N., Jain, M.K. MRA-VC: multiple resources aware virtual machine consolidation using particle swarm optimization. Int. j. inf. tecnol. 15, 697–710 (2023). https://doi.org/10.1007/s41870-022-01102-9
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DOI: https://doi.org/10.1007/s41870-022-01102-9