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.

翻译：数据中心消耗的能源份额日益增长，亟需可持续的资源管理方案。本文提出一种混合蚁群-粒子群算法，用于绿色云数据中心中面向能耗感知的虚拟机部署与迁移。在第一阶段，蚁群优化算法在物理主机间执行节能的初始部署，确保全局可行性。第二阶段采用离散粒子群优化算法，通过从过载或低利用率主机迁移虚拟机来优化资源分配。该混合算法引入多项创新：元启发式算法的顺序混合机制、基于蚁群优化输出的系统化粒子初始化方法、面向约束处理的启发式离散化策略，以及最小化活跃服务器数量与资源浪费的多目标适应度函数。在CloudSimPlus平台实现的仿真实验表明，该算法在各项指标上持续优于经典启发式算法、统一蚁群系统及纯蚁群算法。值得注意的是，在大规模工作负载下，相较于统一蚁群系统，该混合算法可实现高达25%的能耗降低与18%的服务等级协议违规减少，同时保持稳定的成本与碳排放水平。这些结果凸显了两阶段仿生混合策略在应对云资源管理动态性与多目标特性方面的有效性。