Analysis of Virtual Machine Resource Scheduling Strategies in Cloud Computing Environments

With the rapid development of cloud computing technology, effective virtual machine resource scheduling has become a key factor in improving the operational efficiency of data centers. This paper comprehensively evaluates existing resource scheduling algorithms, including traditional polling and priority queuing methods, as well as meta-heuristic based ant colony algorithms, genetic algorithms, and artificial bee colony algorithms, and focuses on analyzing the applicability of these algorithms in cloud environments and the challenges they face. Through case studies, this paper reveals how algorithmic optimization can be used to achieve efficient resource allocation in highly dynamic cloud environments. It is found that although the existing algorithms can optimize resource allocation under specific conditions, they are still deficient in dynamic adaptation, resource conflict handling, computational overhead and multi-objective optimization.