A novel virtual machine placement algorithm based on grey wolf optimization

作者：Hao Feng, Haoyu Li, Yuming Liu, Kun Cao, Xiumin Zhou · 发表于：Journal of Cloud Computing Advances Systems and Applications · 年份：2025 · DOI：10.1186/s13677-025-00730-3 · 被引用次数：10 · 研究领域：Scheduling and Optimization Algorithms、VLSI and FPGA Design Techniques、Cloud Computing and Resource Management

Virtual machine placement is a typical NP-hard problem in the field of cloud computing. Unreasonable placement schemes can result in low resource utilization and high energy consumption. To address these issues, this paper proposes a heuristic algorithm named Adaptive Boundary Grey Wolf Optimization (ABGWO). The objective is to minimize the number of physical servers, and the evaluation is conducted using Microsoft’s Azure Trace dataset from 2020. Comparative experiments are performed with genetic algorithm, particle swarm optimization algorithm, artificial bee colony algorithm, moth search algorithm, and differential evolution algorithms. The experimental results demonstrate that the ABGWO algorithm outperforms other algorithms in terms of solution quality and stability.