面向高效虚拟机放置：一种用于绿色云基础设施的两阶段ACO-PSO方法
Towards Efficient VM Placement: A Two-Stage ACO-PSO Approach for Green Cloud Infrastructure
AI总结：
针对云数据中心能耗高的问题，提出两阶段混合ACO-PSO算法HAPSO，通过初始放置与迁移优化实现节能，仿真显示其性能优于多种对比算法，可显著降低能耗与SLA违规。
摘要：
数据中心的能源消耗占比不断攀升，推动了可持续资源管理的需求。本文提出了一种混合ACO-PSO（HAPSO）算法，用于绿色云数据中心中感知能耗的虚拟机（VM）放置与迁移。第一阶段，蚁群优化（ACO）在物理主机间执行节能的初始放置，确保全局可行性。第二阶段，离散粒子群优化（PSO）通过从过载或未充分利用的主机迁移虚拟机来优化分配。HAPSO引入了多项创新：元启发式算法的顺序混合、利用ACO输出的系统感知粒子初始化、用于约束处理的启发式引导离散化，以及最小化活跃服务器数量和资源浪费的多目标适应度函数。该算法在CloudSimPlus中实现，大量仿真实验表明，HAPSO的性能始终优于经典启发式算法（BFD、FFD）、统一蚁群系统（UACS）和纯ACO算法。值得注意的是，在大规模工作负载下，与UACS相比，HAPSO的能耗降低了高达25%，SLA违规减少了18%，同时保持了稳定的成本和碳排放。这些结果凸显了两阶段仿生混合方法在解决云资源管理的动态性和多目标性方面的有效性。
英文摘要：
Datacenters consume a growing share of energy, prompting the need for sustainable resource management. This paper presents a Hybrid ACO-PSO (HAPSO) algorithm for energy-aware virtual machine (VM) placement and migration in green cloud datacenters. In the first stage, Ant Colony Optimization (ACO) performs energy-efficient initial placement across physical hosts, ensuring global feasibility. In the second stage, a discrete Particle Swarm Optimization (PSO) refines allocations by migrating VMs from overloaded or underutilized hosts. HAPSO introduces several innovations: sequential hybridization of metaheuristics, system-informed particle initialization using ACO output, heuristic-guided discretization for constraint handling, and a multi-objective fitness function that minimizes active servers and resource wastage. Implemented in CloudSimPlus, extensive simulations demonstrate that HAPSO consistently outperforms classical heuristics (BFD, FFD), Unified Ant Colony System (UACS), and ACO-only. Notably, HAPSO achieves up to 25% lower energy consumption and 18% fewer SLA violations compared to UACS at large-scale workloads, while sustaining stable cost and carbon emissions. These results highlight the effectiveness of two-stage bio-inspired hybridization in addressing the dynamic and multi-objective nature of cloud resource management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
