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     

区间多目标优化非支配排序云模型算法

针对区间多目标优化问题,利用云模型对NSGA-II算法进行改进,提出一种非支配排序云模型算法（NSCMA）。首先,从初始云团中随机选取一个云滴作为父代,通过正态云算子生成子代云滴,用来替代传统NSGA-II遗传操作中的交叉和变异;其次,用约束条件对生成的云滴进行筛选处理,避免不可行解进入下一步算法;最后,运用区间占优关系对满足约束条件的解进行占优排序,对无法比较的同序值解计算拥挤距离。仿真结果验证了所设计算法的有效性。     

A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing

Neural Computing and Applications - The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure...     

云数据中心基于贪心模式的虚拟机选择算法

针对如何从云数据中心的异常物理主机中选择出候选迁移虚拟机列表是虚拟机迁移中的问题，提出了基于贪心模式的虚拟机选择算法(GAO-VMS)。GAO-VMS每次都选择那些目标函数最优的虚拟机作为标准来迁移，形成候选迁移虚拟机列表,它有三类贪心模式：最大能量降低消耗策略(MPR)、最小迁移时间及能量消耗均衡策略(TPT)、最小每秒百万条指令数虚拟机请求策略(VVM)。使用CloudSim模拟器作为GAO-VMS的仿真环境。仿真结果表明：与常见的虚拟机迁移策略相比较，GAO-VMS使得云数据中心的能量消耗减少了30%~35%,虚拟机迁移次数减少了40%~45%，服务等级协议(SLA)违规率以及SLA违规和能量消耗联合指标只有5%的增加。GAO-VMS策略可用于企业构造绿色云计算中心。     

Joint VM placement and topology optimization for traffic scalability in dynamic datacenter networks

In dynamic datacenter networks (DDNs), there are two ways to handle growing traffic: adjusting the network topology according to the traffic and placing virtual machines (VMs) to change the workload according to the topology. While previous work only focused on one of these two approaches, in this paper, we jointly optimize both virtual machine placement and topology design to achieve higher traffic scalability. We formulate this joint optimization problem to be a mixed integer linear programming (MILP) model and design an efficient heuristic based on Lagrange’s relaxation decomposition. To handle traffic dynamics, we introduce an online algorithm that can balance algorithm performance and overhead. Our extensive simulation with various network settings and traffic patterns shows that compared with randomly placing VMs in fixed datacenter networks, our algorithm can reduce up to 58.78% of the traffic in the network, and completely avoid traffic overflow in most cases. Furthermore, our online algorithm greatly reduces network cost without sacrificing too much network stability.     

Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization

This paper focuses on the application of a decision support system based on evolutionary multi-objective optimization for deploying sensors in an indoor localization system. Our methods aim to provide the human expert who works as the sensor resource manager with a full set of Pareto efficient solutions of the sensor placement problem. In our analysis, we use five scalar performance measures as objective functions derived from the covariance matrix of the estimation, namely the trace, determinant, maximum eigenvalue, ratio of maximum and minimum eigenvalues, and the uncertainty in a given direction. We run the multi-objective genetic algorithm to optimize these objectives and obtain the Pareto fronts. The paper includes a detailed explanation of every aspect of the system and an application of the proposed decision support system to an indoor infrared positioning system. Final results show the different placement alternatives according to the objectives and the trade-off between different accuracy performance measures can be clearly seen. This approach contributes to the current state-of-the art in the fact that we point out the problems of optimizing a single accuracy measure and propose using a decision support system that provides the resource manager with a full overview of the set of Pareto efficient solutions considering several accuracy metrics. Since the manager will know all the Pareto optimal solutions before deciding the final sensor placement scheme, this method provides more information than dealing with a single function of the weighted objectives. Additionally, we are able to use this system to optimize objectives obtained from fairly complex functions. On the contrary, recent works that are referenced in this paper need to simplify the localization process to obtain tractable problem formulations.     

An interval algorithm for multi-objective optimization

This paper presents an interval algorithm for solving multi-objective optimization problems. Similar to other interval optimization techniques, [see Hansen and Walster (2004)], the interval algorithm presented here is guaranteed to capture all solutions, namely all points on the Pareto front. This algorithm is a hybrid method consisting of local gradient-based and global direct comparison components. A series of example problems covering convex, nonconvex, and multimodal Pareto fronts is used to demonstrate the method.     

基于Kalman滤波的云数据中心能耗和性能优化

云数据中心虚拟机的动态整合需要跟踪服务器的运行状态,而服务器的运行状态会受到数据中心负载变化的影响,现有的CPU使用率预测方法大都只关注当前服务器的CPU利用率变化。提出了一个基于Kalman滤波的CPU使用率预测模型,建立了基于所有服务器CPU使用率变化系数的数据中心负载变化模型,详细描述了基于Kalman滤波的CPU使用率预测方法,讨论了云数据中心的能耗和性能评价指标。最后,为了验证基于Kalman滤波的CPU使用率预测算法的有效性,在CloudSim仿真系统和PlanetLab的五个数据集上进行了实验。实验结果表明,Kalman滤波能够较好地反映服务器CPU使用率的变化趋势,有效地降低数据中心的能耗,并保持较好的计算性能。     

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。     

An energy-efficient cuckoo search algorithm for virtual machine placement in cloud computing data centers

The Journal of Supercomputing - The advent of virtualization technology has created a huge potential application for cloud computing. In virtualization, a large hardware resource is often broken...     

云计算中能耗和性能感知的虚拟机优化部署算法

优化虚拟机部署是数据中心降低能耗的一个重要方法。目前大多数虚拟机部署算法都明显地降低了能耗,但过度虚拟机整合和迁移引起了系统性能较大的退化。针对该问题,首先构建虚拟机优化部署模型。然后提出一种二阶段迭代启发式算法来求解该模型,第一阶段是基于首次适应下降装箱算法,提出一种虚拟机优化部署算法,目标是最小化主机数;第二阶段是提出了一种虚拟机在线迁移选择算法,目标是最小化待迁移虚拟机数。实验结果表明,该算法能够有效地降低能耗,具有较低的服务等级协定(SLA)违背率和较好的时间性能。     

混沌云模型多目标布谷鸟搜索算法

针对多目标布谷鸟搜索算法（MOCS）迭代后期寻优速度慢,并且容易造成局部最优等缺点,提出一种混沌云模型多目标布谷鸟搜索算法（CCMMOCS）。首先在进化过程中通过混沌理论对一般的布谷鸟巢位置在全局中寻求优化,以防落入局部最优;然后利用云模型对较好的布谷鸟巢位置局部优化来提高精度;最后将两种方法对比得到相对更好的解作为最优值以完成优化。对比误差估计值及多样性指标,由5个常用多目标测试函数仿真结果可知,CCMMOCS比传统多目标布谷鸟搜索算法、多目标粒子群算法（MOPSO）及多目标遗传（NSGA-Ⅱ）算法性能更好,Pareto前沿更接近理想曲线,分布也更均匀。     

云制造环境下面向多目标优化的虚拟资源调度研究

为解决云制造环境下虚拟资源调度存在的算法求解效率不高、模型建立缺乏考虑任务间关系约束和任务间及子任务间的物流时间及成本因素等不足,构建了兼顾交货期时间最小化、服务成本最低化、服务质量最优化为目标的多目标虚拟资源调度模型;采用一种基于项目阶段的双链编码方式进行编码,并提出自适应交叉与变异概率公式,以避免交叉、变异概率始终不变导致算法效率下降与过早收敛的问题;在此基础上利用基于项目阶段的多种交叉变异策略相结合的改进遗传算法进行求解,保证了算法的全局与局部搜索性能。实例结果表明,相比于传统的模型与算法,该模型适用性更强,改进的遗传算法在求解效率、准确度与稳定性方面均有较大提高。     

云计算中基于多目标优化的动态资源配置方法

目前,云平台的大多数动态资源分配策略只考虑如何减少激活物理节点的数量来达到节能的目的,以实现绿色计算,但这些资源再配置方案很少考虑到虚拟机放置的稳定性。针对应用负载的动态变化特征,提出一种新的面向多虚拟机分布稳定性的基于多目标优化的动态资源配置方法,结合各应用负载的当前状态和未来的预测数据,综合考虑虚拟机重新放置的开销以及新虚拟机放置状态的稳定性,并设计了面向虚拟机分布稳定性的基于多目标优化的遗传算法（MOGANS）进行求解。仿真实验结果表明,相对于面向节能和多虚拟机重分布开销的遗传算法（GA-NN）,MOGANS得到的虚拟机分布方式的稳定时间是GA-NN的10.42倍;同时,MOGANS也较好权衡了多虚拟机分布的稳定性和新旧状态转换所需的虚拟机迁移开销之间的关系。     

An online variable-fidelity optimization approach for multi-objective design optimization

Structural and Multidisciplinary Optimization - Multi-objective genetic algorithms (MOGAs) are effective ways for obtaining Pareto solutions of multi-objective design optimization problems....     

Towards efficient operation of internet data center networks: Joint data placement and flow control for cost optimization

The problem of cost-efficient operation of data center networks used to deliver file sharing services is studied. The aggregate costs are split into server-load-related and link-load-related shares. Thus, the problem of interest is formulated as one of joint data placement and flow control, and mixed integer-linear programming is used to compute the optimal solution. The high complexity of the latter motivated us to design two additional sets of strategies, based on data coding and heuristics, respectively. With coding, a distributed algorithm for the problem is developed. In the simulation experiments, carried out based on actual data center information, network topology and link cost, as well as electricity prices, the advantages of data coding, in particular in the context of multicast, and the impact of different factors such as the network topology and service popularity, on the total cost incurred by all considered strategies, are examined. Network coding with multicast is shown to provide cost savings in the order of 30–80%, depending on the specific context under consideration, relative to the other optimization strategies and heuristic methods examined in this work.     

云环境中多目标优化的虚拟机放置算法

虚拟机放置（VMP）是虚拟机整合的核心,是一个多资源约束的多目标优化问题。高效的VMP算法不仅能显著地降低云数据中心能耗、提高资源利用率,还能保证服务质量（QoS）。针对数据中心能耗高和资源利用率低的问题,提出了基于离散蝙蝠算法的虚拟机放置（DBA-VMP）算法。首先,把最小化能耗和最大化资源利用率作为优化目标,建立多目标约束的VMP优化模型;然后,通过效仿人工蚁群在觅食过程中共享信息素的机制,将信息素反馈机制引入蝙蝠算法,并对经典蝙蝠算法进行离散化改进;最后,用改进的离散蝙蝠算法求解模型的Pareto最优解。实验结果表明,与其他多目标优化的VMP算法相比,所提算法在使用不同数据集的情况下都能有效降低能耗,提高资源利用率,实现了在保证QoS的前提下的降低能耗和提高资源利用率两者之间的优化平衡。     

Virtual machine consolidated placement based on multi-objective biogeography-based optimization

Virtual machine placement (VMP) is an important issue in selecting most suitable set of physical machines (PMs) for a set of virtual machines (VMs) in cloud computing environment. VMP problem consists of two sub problems: incremental placement (VMiP) problem and consolidated placement (VMcP) problem. The goal of VMcP is to consolidate the VMs to more suitable PMs. The challenge in VMcP problem is how to find optimal solution effectively and efficiently especially when VMcP is a kind of NP-hard problem. In this paper, we present a novel solution to the VMcP problem called VMPMBBO. The proposed VMPMBBO treats VMcP problem as a complex system and utilizes the biogeography-based optimization (BBO) technique to optimize the virtual machine placement that minimizes both the resource wastage and the power consumption at the same time. Extensive experiments have been conducted using synthetic data from related literature and data from two real datasets. First of all, the necessity of VMcP has been proved by experimental results obtained by applying VMPMBBO. Then, the proposed method is compared with two existing multi-objective VMcP optimization algorithms and it is shown that VMPMBBO has better convergence characteristics and is more computationally efficient as well as robust. And then, the issue of parameter setting of the proposed method has been discussed. Finally, adaptability and extensibility of VMPMBBO have also been proved through experimental results. To the best of our knowledge, this work is the first approach that applies biogeography-based optimization (BBO) to virtual machine placement.