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.     

云环境下基于模糊隶属度的虚拟机放置算法

虚拟机放置问题是云数据中心资源调度的核心问题之一,它对数据中心的性能、资源利用率和能耗有着重要的影响。针对此问题,以降低数据中心能耗、改善资源利用率和保证服务质量(QoS)为优化目标,借助模糊聚类的思想提出了一种基于模糊隶属度的虚拟机放置算法。首先,结合物理主机过载概率和虚拟机与物理主机之间的相适性放置关系,提出了新的距离度量方法;然后,根据模糊隶属度函数计算得出虚拟机与物理主机之间的相适性模糊隶属度矩阵;最后,借助能耗感知机制,在模糊隶属度矩阵中进行局部搜索从而获得迁移虚拟机的最优放置方案。仿真实验结果表明,提出的算法在降低云数据中心能耗、改善资源利用率和保证QoS方面表现比较优异。     

Bayes Theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

This paper proposes an algorithm for scheduling Virtual Machines (VM) with energy saving strategies in the physical servers of cloud data centers. Energy saving strategy along with a solution for productive resource utilization for VM deployment in cloud data centers is modeled by a combination of “Virtual Machine Scheduling using Bayes Theorem” algorithm (VMSBT) and Virtual Machine Migration (VMMIG) algorithm. It is shown that the overall data center’s consumption of energy is minimized with a combination of VMSBT algorithm and Virtual Machine Migration (VMMIG) algorithm. Virtual machine migration between the active physical servers in the data center is carried out at periodical intervals as and when a physical server is identified to be under-utilized. In VM scheduling, the optimal data centers are clustered using Bayes Theorem and VMs are scheduled to appropriate data center using the selection policy that identifies the cluster with lesser energy consumption. Clustering using Bayes rule minimizes the number of server choices for the selection policy. Application of Bayes theorem in clustering has enabled the proposed VMSBT algorithm to schedule the virtual machines on to the physical server with minimal execution time. The proposed algorithm is compared with other energy aware VM allocations algorithms viz. “Ant-Colony” optimization-based (ACO) allocation scheme and “min-min” scheduling algorithm. The experimental simulation results prove that the proposed combination of ‘VMSBT’ and ‘VMMIG’ algorithm outperforms other two strategies and is highly effective in scheduling VMs with reduced energy consumption by utilizing the existing resources productively and by minimizing the number of active servers at any given point of time.     

云数据中心基于贪心算法的虚拟机迁移策略

为了节省云数据中心的能量消耗,提出了几种基于贪心算法的虚拟机（VM）迁移策略。这些策略将虚拟机迁移过程划分为物理主机状态检测、虚拟机选择和虚拟机放置三个步骤,并分别在虚拟机选择和虚拟机放置步骤中采用贪心算法予以优化。提出的三种迁移策略分别为：最小主机使用效率选择且最大主机使用效率放置算法MinMax_Host_Utilization、最大主机能量使用选择且最小主机能量使用放置算法MaxMin_Host_Power_Usage、最小主机计算能力选择且最大主机计算能力放置算法MinMax_Host_MIPS。针对物理主机处理器使用效率、物理主机能量消耗、物理主机处理器计算能力等指标设置最高或者最低的阈值,参考贪心算法的原理,在指标上超过或者低于这些阈值范围的虚拟机都将进行迁移。利用CloudSim作为云数据中心仿真环境的测试结果表明,基于贪心算法的迁移策略与CloudSim中已存在的静态阈值迁移策略和绝对中位差迁移策略比较起来,总体能量消耗少15%,虚拟机迁移次数少60%,平均SLA违规率低5%。     

利用遗传算法完成虚拟机放置策略的优化

提出基于遗传算法的虚拟机放置方法GA-VMP(Genetic Algorithm based Virtual Machine Placement)。GA-VMP是一种应用于虚拟机迁移过程的优化算法。在物理主机状态检测和虚拟机选择阶段分别选取了鲁棒局部归约检测方法和最小迁移时间选择方法;在最后的虚拟机放置阶段,GA-VMP将遗传算法应用到虚拟机的重新分配过程中形成了一个全新的虚拟机迁移模型。设计云数据中心的能量消耗数学模型,以能量消耗最小作为遗传算法的目标函数。Cloudsim模拟器仿真结果表明：在总体能量消耗、虚拟机迁移次数、服务等级协议违规率等指标上明显降低,平衡指标参数只有少量的增加。仿真结果可为其他企业构造节能云数据中心提供参考作用。     

云计算环境下基于多目标优化的虚拟机放置研究

云数据中心的规模日益增长导致其产生的能源消耗及成本呈指数级增长。虚拟机的放置是提高云计算环境服务质量与节约成本的核心。针对传统的虚拟机放置算法存在考虑目标单一化和多目标优化难以找到最优解的问题,提出一种面向能耗、资源利用率、负载均衡的多目标优化虚拟机放置模型。通过改进蚁群算法求解优化模型,利用其信息素正反馈机制和启发式搜索寻找最优解。实验结果表明,该算法综合性能表现良好,符合云环境对高效率低能耗的要求。     

云计算数据中心虚拟机节能管理机制研究

云计算数据中心的耗电量巨大,但绝大多数的云计算数据中心并没有取得较高的资源利用率,通常只有15%-20%,有相当数量的服务器处于闲置工作状态,导致大量的能耗白白浪费。为了能够有效降低云计算数据中心的能耗,提出了一种适用于异构集群系统的云计算数据中心虚拟机节能调度算法(PVMAP算法),仿真实验结果表明：与经典算法PABFD相比,PVMAP算法的能耗明显更低,可扩展性与稳定性都更好。与此同时,随着〈Hosts,VMs〉数目的不断增加,PVMAP 算法虚拟机迁移总数和关闭主机总数的增长幅度都要低于PABFD算法。     

Adaptive Markov-based approach for dynamic virtual machine consolidation in cloud data centers with quality-of-service constraints

Dynamic virtual machine (VM) consolidation is one of the emerging technologies that has been considered for low-cost computing in cloud data centers. Quality-of-service (QoS) assurance is one of the challenging issues in the VM consolidation problem since it is directly affected by the increase of resource utilization due to the consolidations. In this paper, we take advantage of Markov chain models to propose a novel approach for VM consolidation that can be used to explicitly set a desired level of QoS constraint in a data center to ensure the QoS goals while improving system utilization. For this purpose, an energy-efficient and QoS-aware best fit decreasing algorithm for VM placement is proposed, which considers QoS objective when determining the location of a migrating VM. This algorithm employs an online transition matrix estimator method to deal with the nonstationary nature of real workload data. We also propose new policies for detecting overloaded and underloaded hosts. The performance of our proposed algorithms is evaluated through simulations. The results show that the proposed VM consolidation algorithms in this paper outperforms the benchmark algorithms in terms of energy consumption, service-level agreement violations, and other cost factors.     

基于双适应度遗传算法的虚拟机放置的研究

减少数据中心产生的网络时延以及优化数据中心能源消耗和物理资源的浪费等越来越受到研究者的关注。主要关注数据中心的物理资源的浪费和数据中心产生的网络时延,并且建模一个多目标优化问题:最小化数据中心的物理资源以及数据中心的时延。通过改进型双适应度遗传算法将两个目标同时优化,将其结果与贪心算法进行比较,实验结果表明,此算法优于贪心算法,是云环境下有效的虚拟机放置算法。     

虚拟机迁移中一种新的物理主机异常状态检测算法

提出了一种新的物理主机异常状态检测算法PHSDA(Physical host status anomalous detection algorithm)。PHSDA算法包括两个阶段;在超负载检测中,采用一种迭代权重线性回归方法来预测物理资源的使用效率情况;在低负载检测中,利用多维物理资源的均方根来确定其资源使用阈值下限,避免异常状态的物理主机数量的增加; PHSDA检测算法配合迁移过程中后续的虚拟机选择策略和虚拟机放置策略,就可以形成一个全新的虚拟机迁移模型PHSDA-MMT-BFD。以CloudSim模拟器作为PHSDA的仿真环境。经PHSDA策略优化过后的新虚拟机迁移实验表明：比近几年的BenchMark迁移模型比较起来,可以很好的降低云数据中心的能量消耗,虚拟机迁移次数减少,云服务质量明显提高。     

DDBS:云数据中心基于数据依赖的虚拟机选择策略

提出了一种云数据中心基于数据依赖的虚拟机选择算法DDBS(data dependency based VM selection).参考Cloudsim项目中方法,将虚拟机迁移过程划分为虚拟机选择操作(VM selection)和虚拟机放置(VM placement)操作.DDBS在虚拟机选择过程中考虑虚拟机之间的数据依赖关系,把选择与迁移代价值比较小的虚拟机形成侯选虚拟机列表,配合后续的虚拟机放置策略最终完成虚拟机的迁移过程.以Cloudsim云计算模拟器中的虚拟机选择及放置策略作为性能比较对象.实验结果表明:DDBS与Cloudsim中已有能量感知的算法比较起来,在虚拟机迁移次数和能量消耗方面都比较少,可用性比较高.     

基于数据中心的两阶段虚拟机能效优化部署算法

针对数据中心在虚拟机动态部署过程中的高能耗问题,提出了面向数据中心的两阶段虚拟机能效优化部署算法--DVMP_VMMA。第一阶段为初始部署,提出了动态虚拟机部署(DVMP)算法限定主机最优部署数量,降低了闲置能耗;同时,为了应对负载的动态变化,第二阶段提出迁移约束的虚拟机迁移算法(VMMA)对初始部署方案作进一步优化,这样不仅得到的系统能耗更低,而且还能保证应用服务质量。与满载算法(FL)、基于固定门限值的部署算法(FT),绝对中位差部署算法(MAD)、四分位差部署算法(QD)、迁移周期最优算法(MTM)、最小占用率迁移算法(MIU)进行的比较实验结果表明:DVMP_VMMA不仅考虑了系统能耗优化,使运行时资源利用率更高;而且还可以避免VM频繁迁移完成对性能的提升,其在优化数据中心能耗、SLA违例、VM迁移量的控制及性能损失等指标上均有较好效果,其综合性能优于对比算法。     

虚拟机放置问题的研究综述

随着云计算数据中心规模和数量的日益增长,以及虚拟化技术的普遍采用,虚拟机放置问题逐步成为产业界和学术界研究的热点。虚拟机放置策略和方法的选择对数据中心的能耗,物理资源的利用率和虚拟机性能具有重大影响。合理的放置方法和策略在保证上层应用和业务不受影响的同时,能有效降低云计算数据中心的能耗,提升物理资源利用率,减少物理资源的浪费。阐述了虚拟机放置问题中的3个基本要素:优化目标、约束限制和实现方法,并基于已有的研究工作进行归纳与总结。最后,结合已有成果,展望了未来的研究方向和亟待解决的关键问题。     

A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Cloud-based data centers consume a significant amount of energy which is a costly procedure. Virtualization technology, which can be regarded as the first step in the cloud by offering benefits like the virtual machine and live migration, is trying to overcome this problem. Virtual machines host workload, and because of the variability of workload, virtual machines consolidation is an effective technique to minimize the total number of active servers and unnecessary migrations and consequently improves energy consumption. Effective virtual machine placement and migration techniques act as a key issue to optimize the consolidation process. In this paper, we present a novel virtual machine consolidation technique to achieve energy–QoS–temperature balance in the cloud data center. We simulated our proposed technique in CloudSim simulation. Results of evaluation certify that physical machine temperature, SLA, and migration technique together control the energy consumption and QoS in a cloud data center.     

云数据中心能效可靠的虚拟机部署

优化虚拟机部署是降低云数据中心能耗的有效方法,但是,过度对虚拟机部署进行合并可能导致主机机架出现热点,影响数据中心提供服务的可靠性。提出一种基于能效和可靠性的虚拟机部署算法。综合考虑主机利用率、主机温度、主机功耗、冷却系统功耗和主机可靠性间的相互关系,建立确保主机可靠性的冗余模型。在主动避免机架热点情况下,实现动态的虚拟机部署决策,在降低数据中心总体能耗前提下,确保主机服务可靠性。仿真结果表明,该算法不仅可以节省更多能耗,避免热点主机,而且性能保障上也更好。     

A cloud middleware for assuring performance and high availability of soft real-time applications

Applications are increasingly being deployed in the cloud due to benefits stemming from economy of scale, scalability, flexibility and utility-based pricing model. Although most cloud-based applications have hitherto been enterprise-style, there is an emerging need for hosting real-time streaming applications in the cloud that demand both high availability and low latency. Contemporary cloud computing research has seldom focused on solutions that provide both high availability and real-time assurance to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers. This paper makes three contributions to address this dual challenge. First, it describes an architecture for a fault-tolerant framework that can be used to automatically deploy replicas of virtual machines in data centers in a way that optimizes resources while assuring availability and responsiveness. Second, it describes the design of a pluggable framework within the fault-tolerant architecture that enables plugging in different placement algorithms for VM replica deployment. Third, it illustrates the design of a framework for real-time dissemination of resource utilization information using a real-time publish/subscribe framework, which is required by the replica selection and placement framework. Experimental results using a case study that involves a specific replica placement algorithm are presented to evaluate the effectiveness of our architecture.     

云数据中心基于遗传算法的虚拟机迁移模型

提出云数据中心中基于遗传算法的虚拟机迁移模型GA-VMM（genetic algorithm based virtual machine migration）。GA-VMM在虚拟机迁移的时刻考虑的问题维度优于常见的策略,使虚拟机的分配与迁移更加合理与公平。建立了云端能量消耗与在线虚拟机迁移时间消耗数学模型,通过全局遗传算法来优化虚拟机迁移和放置策略。利用某个企业的大数据中心作为云端测试环境,对比测试GA-VMM迁移模型与已有的虚拟机迁移策略的性能。测试结果表明,GA-VMM迁移模型能够更好地减少物理主机的使用数量和虚拟机的迁移次数,SLA（service level agreement violation）违规基本处于稳定状态;GA-VMM可以降低数据中心能耗,性能优于已有的迁移策略。     

面向移动云计算的自适应虚拟机调度算法

随着移动云计算的快速发展和应用普及,如何对移动云中心资源进行有效管理同时又降低能耗、确保资源高可用是目前移动云计算数据中心的热点问题之一.本文从CPU、内存、网络带宽和磁盘四个维度,建立了基于多目标优化的虚拟机调度模型VMSM-EUN(Virtual Machine Scheduling Model based on Energy consumption,Utility and minimum Number of servers),将最小化数据中心能耗、最大化数据中心效用以及最小化服务器数量作为调度目标.设计了基于改进粒子群的自适应参数调整的虚拟机调度算法VMSA-IPSO(Virtual Machine Scheduling Algorithm based on Improved Particle Swarm Optimization)来求解该模型.最后通过仿真实验验证了本文提出的调度算法的可行性与有效性.对比实验结果表明,本文设计的基于改进粒子群的自适应虚拟机调度算法在进行虚拟机调度时,能在降低能耗的同时提高数据中心效用.     

Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach

Energy efficiency has grown into a latest exploration area of virtualized cloud computing paradigm. The increase in the number and the size of the cloud data centers has propagated the need for energy efficiency. An extensively practiced technology in cloud computing is live virtual machine migration and is thus focused in this work to save energy. This paper proposes an energy-aware virtual machine migration technique for cloud computing, which is based on the Firefly algorithm. The proposed technique migrates the maximally loaded virtual machine to the least loaded active node while maintaining the performance and energy efficiency of the data centers. The efficacy of the proposed technique is exhibited by comparing it with other techniques using the CloudSim simulator. An enhancement in the average energy consumption of about 44.39 % has been attained by reducing an average of 72.34 % of migrations and saving 34.36 % of hosts, thereby, making the data center more energy-aware.     

A thermal-aware VM consolidation mechanism with outage avoidance

Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal-aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.