用遗传算法动态调度虚拟机，省电又保服务品质。
Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance
- 基于遗传算法动态优化虚拟机部署与合并策略。
- 能耗降低，迁移次数减少，服务违规率显著下降。
- 适合关注绿色云计算与服务质量保障的工程师。
云环境需要动态高效的资源管理以实现最佳性能、降低能耗并满足服务等级协议（SLAs）。本文提出一种基于遗传算法（GA）的虚拟机（VM）部署与整合方法，旨在最小化功耗的同时维持服务质量（QoS）约束。该方法根据实时工作负载变化动态调整虚拟机分配，优于传统启发式算法如首次适应递减（FFD）和最佳适应递减（BFD）。实验结果表明，该方法在能耗、虚拟机迁移次数、SLA违规率及执行时间方面均有显著改善。相关性热力图进一步揭示了这些关键指标间的强关联，验证了本方法在优化云资源利用方面的有效性。
原文摘要 · Abstract (English)
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.
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