An energy-saving virtual machine scheduling algorithm for resource management based on cloud computing technology
To solve the problem of imbalanced resource load in virtual machine clusters, an energy-saving virtual machine scheduling algorithm based on cloud computing technology for resource management is proposed. In this paper, the current research status of cloud computing and virtual machine scheduling in...
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-04-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0244610 |
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| Summary: | To solve the problem of imbalanced resource load in virtual machine clusters, an energy-saving virtual machine scheduling algorithm based on cloud computing technology for resource management is proposed. In this paper, the current research status of cloud computing and virtual machine scheduling in cloud computing environments is analyzed, and the concept and characteristics, classification, application scenarios, and key technologies of cloud computing are elaborated. This paper innovatively designs a universal chromosome structure with regions to adapt to different data center server compositions and introduces adaptive mutation operators based on genetic algorithms to improve global search capabilities and optimize virtual machine scheduling schemes. In addition, by restricting the migration of virtual machines between homogeneous physical machines, the energy loss during the migration process can be reduced, and a more energy-efficient virtual machine physical machine mapping scheme can be further calculated. Finally, by collecting real data on virtual machine loads in reality, the algorithm proposed in this paper is experimentally validated using the CloudSim cloud computing simulation platform. The experimental results show that, in the same original configuration scheme, the migration times based on the greedy algorithm used by GA2ND are around 1000, while the migration times of GA1ST are between 200 and 500, indicating that the migration scheme of GA2ND requires fewer virtual machines than that of GA1ST. Therefore, the algorithm proposed in this paper can effectively reduce energy consumption while avoiding frequent migration of virtual machines, and the innovation in genetic algorithm optimization strategy improves the overall efficiency and stability of scheduling. |
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| ISSN: | 2158-3226 |