基于混合群智能的节能虚拟机整合方法

数据中心的虚拟机（virtual machine，VM）整合技术是当今云计算领域的一个研究热点.要在保证服务质量（QoS）的前提下尽可能地降低云数据中心的服务器能耗，本质上是一个多目标优化的NP难问题.为了更好地解决该问题，面向异构服务器云环境提出了一种基于差分进化与粒子群优化的混合群智能节能虚拟机整合方法（HSI-VMC）.该方法包括基于峰值效能比的静态阈值超载服务器检测策略（PEBST）、基于迁移价值比的待迁移虚拟机选择策略（MRB）、目标服务器选择策略、混合离散化启发式差分进化粒子群优化虚拟机放置算法（HDH-DEPSO）以及基于负载均值的欠载服务器处理策略（AVG）.其中，PEBST，MRB，AVG策略的结合能够根据服务器的峰值效能比和CPU的负载均值检测出超载和欠载服务器，并选出合适的虚拟机进行迁移，降低负载波动引起的服务水平协议违约率（SLAV）和虚拟机迁移的次数；HDH-DEPSO算法结合DE和PSO的优点，能够搜索出更优的虚拟机放置方案，使服务器尽可能地保持在峰值效能比下运行，降低服务器的能耗开销.基于真实云环境数据集（PlanetLab/Mix/Gan）的一系列实验结果表明：HSI-VMC方法与当前主流的几种节能虚拟机整合方法相比，能够更好地兼顾多个QoS指标，并有效地降低云数据中心的服务器能耗开销.

Energy Efficient Hybrid Swarm Intelligence Virtual Machine Consolidation Method

Virtual machine (VM) consolidation for cloud data centers is one of the hottest research topics in cloud computing. It is challenging to minimize the energy consumption while ensuring QoS of the hosts in cloud data centers, which is essentially an NP-hard multi-objective optimization problem. This study proposes an energy efficient hybrid swarm intelligence virtual machine consolidation method (HSI-VMC) for heterogeneous cloud environments to address this issue, which including peak efficiency based static threshold overloaded hosts detection strategy (PEBST), migration ratio based reallocate virtual machine selection strategy (MRB), target host selection strategy, hybrid discrete heuristic differential evolutionary particle swarm optimization virtual machine placement algorithm (HDH-DEPSO) and load average based underloaded hosts processing strategy (AVG). Specifically, the combination of PEBST, MRB, and AVG is able to detect the overloaded and underloaded hosts and selects appropriate virtual machines for migration to reduce SLAV and virtual machine migrations. Also, HDH-DEPSO combines the advantages of DE and PSO to search the best virtual machine placement solution, which can reduce cluster''s real-time power effectively. A series of experiments based on real cloud environment datasets (PlanetLab, Mix, and Gan) show that HSI-VMC can reduce energy consumption sharply with accommodate to multiple QoS metrics, outperforms several existing mainstream energy-aware virtual machine consolidation approaches.