基于混合粒子群算法的虚拟数据中心能耗优化

针对当前数据中心节能整合研究中仅考虑服务器能耗的现状,提出一种同时考虑服务器和网络设备能耗的方法。该方法通过感知数据中心的网络拓扑,使运行的服务器和网络设备最少,以此最小化能耗。对问题进行新的形式化建模,并设计一种混合的粒子群优化算法HPSO-NA来实现虚拟机整合。实验结果表明,该方法能有效降低整体能耗。     

基于MapReduce虚拟集群的能耗优化算法

随着全球能源危机的出现,许多研究者开始关注数据中心的能耗问题。在满足用户需求的前提下,减少数据中心的活跃节点个数能够有效地降低其能耗。传统的减少活跃节点的方式是虚拟机迁移,但虚拟机迁移会造成极大的系统开销。提出一种基于MapReduce虚拟集群的能耗优化算法--在线时间平衡算法OTBA,能够减少活跃物理节点数,有效降低数据中心的能耗,并且避免了虚拟机的迁移。通过建立云数据中心的能耗模型、用户提交服务的排队模型和评价作业完成质量的作业运行模型,确定了数据中心节能模型的目标函数和变量因子。在线时间平衡算法是基于虚拟云环境和在线MapReduce作业的一种节能调度算法,能够在虚拟机的生命周期和资源利用率之间做出权衡,使数据中心激活的服务器达到最少,能耗降到最低。此外,该结果通过仿真和Hadoop平台上的实验得到了验证。     

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。     

可靠性感知下的虚拟数据中心映射算法

介绍现阶段虚拟数据中心(VDC)映射的研究进展,根据租户对VDC可靠性的需求,提出一种可靠性感知下的VDC映射启发式算法。对于每个VDC,该算法通过限制能放置在同一个服务器上的最大虚拟机数目来保证租户VDC可靠性需求,然后以降低数据中心网络带宽消耗和服务器能耗为主要目标进行VDC映射。其具体做法是:首先将相互之间带宽需求量大的虚拟机合并部署来降低数据中心网络带宽的消耗;然后把合并后的虚拟机优先部署到已开启的服务器上,从而减少开启的服务器数目,降低数据中心的服务器能耗。利用基于胖树结构的数据中心拓扑对提出的算法进行了仿真,结果表明,与2EM算法相比,该算法能够满足租户VDC的可靠性需求,能在不增加额外能耗的前提下最多减少数据中心网络约30%的带宽消耗。     

基于动态调整阈值的虚拟机迁移算法

针对当前数据中心服务器能耗优化和虚拟机迁移时机合理性问题,提出一种基于动态调整阈值（DAT）的虚拟机迁移算法。该算法首先通过统计分析物理机历史负载数据动态地调整虚拟机迁移的阈值门限,然后通过延时触发和预测物理机的负载趋势确定虚拟机迁移时机。最后将该算法应用到实验室搭建的数据中心平台上进行实验验证,结果表明基于DAT的虚拟机迁移算法比静态阈值法关闭的物理机数量更多,云数据中心能耗更低。基于DAT的虚拟机迁移算法能根据物理机的负载变化动态迁移虚拟机,达到提高物理机资源利用率、降低数据中心能耗、提高虚拟机迁移效率的目的。     

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.     

面向数据中心的虚拟机部署及优化算法

虚拟机动态配置是解决数据中心能耗低效的有效方法。针对动态配置过程中的虚拟机部署及优化问题展开研究,提出一种新的面向系统能耗的虚拟机部署算法以及基于主动迁移的优化策略。为了降低系统能耗,新算法采用基于服务器利用率的最佳适配降序算法求解虚拟机部署方案;同时为了适应应用负载的动态变化,新算法启动主动迁移策略对部署方案进行优化,即通过启发式算法在当前部署的基础上搜索使系统能耗更低的优化方案,并根据新部署对虚拟机执行主动迁移。考虑到迁移会导致应用服务质量降级和额外能耗,新算法通过在优化策略中设置基于服务器利用率的启动门限,对虚拟机主动迁移频率进行控制。仿真实验表明,所提算法在系统能耗、虚拟机迁移频率、服务器状态切换频率以及服务质量等多项性能指标上均有显著提高     

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.     

数据中心虚拟机节能管理机制

大规模数据中心需要消耗大量的电能,由此带来了高额的运营成本以及环境污染等问题。为了降低数据中心的能耗,在构造了数据中心管理模型的基础上,提出了虚拟机静态安置算法与动态调整算法。虚拟机的动态迁移技术能够有效地降低数据中心能耗,提升资源利用率。然而,过度地迁移虚拟机,会影响应用的运行质量,造成SLA违背。动态调整阶段,采用了动态阈值的方法来控制虚拟机的迁移,降低能耗。最后,利用CloudSim平台进行了大量的模拟实验。实验结果表明,所提出的数据中心虚拟机节能管理机制(EAMVM)能够降低能源消耗,减少虚拟机的迁移次数。     

面向数据中心能效优化的虚拟机迁移调度方法

由于服务器资源利用率偏低且资源负载不均衡,使得数据中心能耗浪费严重。针对上述情况,提出基于虚拟机迁移的数据中心节能调度方法。该方法通过选择合适的迁移时机、迁移对象和目标主机,完成虚拟机迁移前的准备工作,然后基于迭代-停止迁移方法对服务器进行动态迁移和整合,从而减少服务器的运行数量,以此最小化数据中心能耗。实验结果表明,该方法能有效提高服务器资源利用率,减少服务器的冗余数量,提高数据中心整体能效。     

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.     

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Live Virtual Machine (VM) consolidation is an effective method of improving energy-efficiency level in green data centers. Currently, to evaluate energy consumption in green data centers, energy-efficiency evaluation model with CPU utilization rate has been proposed. However, it is not suitable for data-intensive computing due to great energy consumption by GPU-intensive processing. In this paper, we have proposed a new energy evaluation model with CPU and GPU utilization rates. There are two kinds of policies in live VM consolidation: one for VM selection and the other for VM allocation. Some researchers have proposed their solutions based on VM selection policy or VM allocation policy respectively. However, it will be a better energy-efficiency VM consolidation policy if these two polices are integrated together. Based on these two policies, a fast Artificial Bee Colony (ABC) based energy-efficiency live VM consolidation policy with data-intensive energy model, named as DataABC, is proposed. DataABC adopts the idea of Artificial Bee Colony algorithm to get a fast and global optimized decision of VM consolidation. Compared with two state-of-art policies of PS-ABC and PS-ES, the total energy consumption of DataABC evidently drop by 9.72% and 5.84% respectively. As a result, based on the ESV metric, the DataABC approach has proved that (a) the energy-efficiency evaluation model with data-intensive computing is valid and that (b) DataABC can save energy with a good Quality of Service (QoS) in green data centers.     

基于自适应过载阈值选择的虚拟机动态整合方法

针对云环境下动态工作负载的不确定性,提出了基于自适应过载阈值选择的虚拟机动态整合方法。为了权衡数据中心能源有效性与服务质量间的关系,将自适应过载阈值的选择问题建模为马尔可夫决策过程,计算过载阈值的最优选择策略,并根据系统能效和服务质量调整阈值。通过过载阈值检测过载物理主机,然后根据最小迁移时间原则以及最小能耗增加放置原则确定虚拟机的迁移策略,最后切换轻负载物理主机至休眠状态完成虚拟机整合。仿真实验结果表明,所提出的方法在减少虚拟机迁移次数方面效果显著,在节约数据中心能源开销与保证服务质量方面表现良好,在能源的有效性与云服务质量二者之间取得了比较理想的平衡。     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.     

资源调度等待开销感知的虚拟机整合

近年来,数据中心庞大的能源开销问题引起广泛关注.虚拟化管理平台可以通过虚拟机迁移技术将虚拟机整合到更少的服务器上,从而提高数据中心能源有效性.对面向数据中心节能的虚拟机整合研究工作进行调研,并总结虚拟机整合研究存在的3个挑战.针对已有工作未考虑虚拟机等待资源调度带来的服务器资源额外开销这种现象,开展了资源调度等待开销感知的虚拟机整合研究.从理论和实验上证明了在具有实际意义的约束条件下,存在着虚拟机等待资源调度带来的服务器资源额外开销,且随着整合虚拟机数量的增长保持稳定.基于典型工作负载的实验结果表明,这个额外开销平均占据了11.7%的服务器资源开销.此外,提出了资源预留整合（MRC）算法,用于改进已有的虚拟机整合算法.算法模拟实验结果表明,MRC算法相比于常用的虚拟机整合算法FFD（first fit decreasing）,明显降低了服务器资源溢出概率.     

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。     

Heuristics based server consolidation with residual resource defragmentation in cloud data centers

Server Consolidation is one of the foremost concerns associated with the effective management of a Cloud Data Center as it has the potential to accomplish significant reduction in the overall cost and energy consumption. Most of the existing works on Server Consolidation have focused only on reducing the number of active physical servers (PMs) using Virtual Machine (VM) Live Migration. But, along with reducing the number of active PMs, if a consolidation approach reduces residual resource fragmentation, the residual resources can be efficiently used for new VM allocations, or VM reallocations, and some future migrations can also be reduced. None of the existing works have explicitly focused on reducing residual resource fragmentation along with reducing the number of active PMs to the best of our knowledge. We propose RFAware Server Consolidation, a heuristics based server consolidation approach which performs residual resource defragmentation along with reducing the number of active PMs in cloud data centers.