Cloud computing environments demand dynamic and efficient resource management to ensure optimal performance, reduced energy consumption, and adherence to Service Level Agreements (SLAs). This paper presents a Genetic Algorithm (GA)-based approach for Virtual Machine (VM) placement and consolidation, aiming to minimize power usage while maintaining QoS constraints. The proposed method dynamically adjusts VM allocation based on real-time workload variations, outperforming traditional heuristics such as First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show notable reductions in energy consumption, VM migrations, SLA violation rates, and execution time. A correlation heatmap further illustrates strong relationships among these key performance indicators, confirming the effectiveness of our approach in optimizing cloud resource utilization.

本研究解决了云计算资源管理中的动态与高效问题，特别是优化虚拟机的配置以减少能耗并确保服务水平协议的遵循。提出的遗传算法方法根据实时工作负载变化动态调整虚拟机分配，显著优于传统启发式方法，实验结果显示在能耗、虚拟机迁移、服务水平协议违反率和执行时间上均有显著减少，验证了其优化云资源利用的有效性。

基于遗传算法的云资源优化分配研究：能源效率与服务质量保障