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。     

FICA: fuzzy imperialist competitive algorithm

Despite the success of the imperialist competitive algorithm （ICA） in solving optimization problems, it still suffers from frequently falling into local minima and low convergence speed. In this paper, a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA, in the proposed algorithm, powerful countries are chosen as imperialists in each step;according to a fuzzy membership function, other countries become colonies of all the empires. In ab-sorption policy, based on the fuzzy membership function, colonies move toward the resulting vector of all imperialists. In this algorithm, no empire will be eliminated;instead, during the execution of the algorithm, empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments, the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.     

高效能云计算虚拟机优化部署策略

针对云计算应用负载需求的动态变化特性,提出了一种自适应虚拟机优化部署策略。算法通过基于强局部加权回归的热点发现机制,可以根据负载所体现的资源占用历史信息动态决策主机的超载时机;通过迁移周期最优算法MPM和迁移量最少算法MNM进行超载主机的迁移虚拟机选择;提出基于功耗感知的PBFDH算法对迁移虚拟机再次优化部署。实验结果表明,算法不仅可以降低能耗,还可以降低SLA违例率。     

基于帝国主义竞争算法的WSNs定位方案

遗传算法(GA)在无线传感器网络(WSNs)定位时存在收敛速度慢、精度低等弊端,针对以上问题,提出了一种利用帝国主义竞争算法(ICA)优化WSNs定位的方案。首先,使用了采样的方法来估计未知节点的初始位置;其次,依靠信标节点和相邻节点的相关信息建立了以最小化全局误差的三维空间的数学定位模型;最后,使用了最新的社会启发算法—ICA来进行定位优化。实验结果表明:与GA定位相比,ICA在WSNs定位上具有定位精度高、收敛迅速的优势。     

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.     

云计算环境下的虚拟机快速克隆技术

虚拟机克隆技术是指在云计算环境下快速复制出多个虚拟机（VM）并将这些VM分发到多台物理主机上,克隆出来的VM共享相同的初始状态然后独立运行提供服务。虚拟机克隆使得云计算提供商能够快速有效地部署系统资源。给出了一种虚拟机快速克隆方法,利用写时拷贝技术来创建虚拟磁盘和内存状态的快照,然后用按需分配内存技术和多点传送技术来请求和传输这些状态信息。在C3云平台上的实验表明,此方法在不中断源虚拟机中运行服务的情况下,实现了云计算中的快速虚拟机克隆。     

Layered virtual machine migration algorithm for network resource balancing in cloud computing

Due to the increasing sizes of cloud data centers, the number of virtual machines (VMs) and applications rises quickly. The rapid growth of large scale Internet services results in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm (LVMM). At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we balance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of network resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMMalgorithm can effectively balance the load of network resource in cloud computing.     

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。     

基于WFPSO算法的云虚拟机放置策略

随着云计算技术的大规模应用,云应用的交互更加依赖于网络,较差网络拓扑的选择,增加了应用在网络中的通信流量,严重影响应用的运行效率和服务质量。为解决此问题,提出了一种基于粒子群优化算法的虚拟机放置策略。该策略通过建立云环境内部时延模型,利用改进的粒子群优化算法求解目标函数,来降低应用的时延,提高运行效率。并在CloudSim平台上进行仿真实验,实验结果表明,该策略的响应时间低于基本粒子群优化算法（PSO）,并且修改后的PSO算法在不影响收敛精度的前提下较大幅度地提高粒子群算法的收敛速度,提高了云环境中应用的运行效率。     

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。     

帝国竞争算法求解CVRP

针对带容量约束的车辆路径问题(CVRP),提出了一种带分裂机制的帝国竞争算法进行求解。首先,结合CVRP的特性,采用基于贪婪准则的编解码策略实现算法空间到解空间的转换。其次,提出帝国分裂策略来增强算法的全局搜索能力,并结合2-Opt提高算法的局部搜索能力。最后,通过25个基准算例的仿真实验表明:所提算法能有效求解CVRP,所有算例的优化误差不超过1.0%;与已有的帝国竞争算法、粒子群算法、遗传算法、布谷鸟搜索算法相比,所提算法的求解效率更高。     

一种求解旅行商问题的新型帝国竞争算法

帝国竞争算法是一种已在连续优化问题上取得较好效果的新型社会政治算法. 为了使该算法更好地应用于离散型组合优化问题, 提出一种求解旅行商问题的新型帝国竞争算法. 在传统算法的基础上, 改变初始帝国的生成方式; 同化过程采取替换重建方式, 以提升求解质量; 革命过程中引入自适应变异算子, 以增强搜索能力; 殖民竞争过程中调整了殖民地分配方式; 算法加入帝国增强过程, 以加快寻化速度. 实验结果表明, 新型帝国竞争算法求解质量高、收敛速度快.

     

Network-aware virtual machine migration in an overcommitted cloud

Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention.However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center.This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs.The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem.     

云计算中面向能耗降低的虚拟机多资源放置算法

为降低云计算系统产生的能耗,实现系统多类型资源的合理利用,提出虚拟机多资源能耗优化放置模型,并给出虚拟机多目标资源随机多组优化算法(RMRO)。RMRO算法随机生成多组虚拟机放置序列,并对每组序列进行优化,从中选出最优的序列作为最终的虚拟机序列。基于RMRO,进一步提出了3种虚拟机放置序列的再优化策略,通过实验对比,选择MMBA策略作为最佳策略。仿真结果表明,RMRO相比传统的MBFD和MBFH算法,能明显降低数据中心的能耗,同时使系统多种资源利用更合理。     

基于新型帝国竞争算法的高维多目标柔性作业车间调度

针对高维多目标柔性作业车间调度问题(MaOFJSP),提出了一种新型帝国竞争算法(ICA)以同时最小化最大完成时间、最大拖期、最大机器负荷和总能耗,该算法采用新方法构建初始帝国使得大多数殖民国家分配数量相近的殖民地,引入殖民国家的同化,并应用新的革命策略和帝国竞争方法以获得高质量解.最后通过大量实验测试ICA新策略对其性能的影响并将ICA与其他算法对比,实验结果表明新型ICA在求解MaOFJSP方面具有较强的优势.     

云环境下基于改进遗传算法的虚拟机调度策略

针对云环境下服务器内部多种资源间分配不均衡问题,提出了一种多维资源协同聚合的虚拟机调度算法MCCA。该算法在分组遗传算法的基础上,采用模糊逻辑及基于资源利用率多维方差的控制参量,设计适应度函数指导搜索解空间。算法使用基于轮盘赌法的选择方法,并对交叉和变异等进行了优化,以实现快速有效地获取近似最优解。在CloudSim环境下进行了仿真,实验结果表明该算法对均衡多维资源分配和提高资源综合利用率具有一定的优势。     

An energy-efficient virtual machine placement and route scheduling scheme in data center networks

The increasing requirements of big data analytics and complex scientific computing impose significant burdens on cloud data centers. As a result, not only the computation but also the communication expenses in data centers are greatly increased. Previous work on green computing in data centers mainly focused on the energy consumption of the servers rather than the communication. However, for those emerging applications with big data-flows transmission, more energy consumption could be consumed by communication links, switching and aggregation elements. To this end, based on data-flows’ transmission characteristics, we proposes a novel Job-Aware Virtual Machine Placement and Route Scheduling (JAVPRS) scheme to reduce the energy consumption of data center networks (DCN) while still meeting as many network QoS (Quality of Service) requirements as possible. Our proposed scheme focuses on not just migrating large data flows, but also integrating small data flows to improve the utilization rate of the communication links. With more idle switches turned off, DCN’s energy consumption will thus be reduced. Besides the data flows’ migration and integration, the Traffic Engineering (TE) technique is also applied to decrease the transmission delay and increase the network throughput. To evaluate the performance of our proposed scheme, a number of simulation studies are performed. Compared to the selected benchmarks, the simulation results showed that JAVPRS can achieve 22.28%–35.72% energy saving while reducing communication delay by 5.8%–6.8% and improving network throughput by 13.3%.     

云计算IPv4/IPv6虚拟机在线迁移系统设计

根据云计算平台的特点,为了实现IPv4/IPv6环境下的云计算基础平台建设,设计了一种基于自适应的NAT-PT与隧道技术协作的IPv4/IPv6虚拟机在线迁移系统。该系统适用于IPv4向IPv6演进的前期、中期、后期。利用设计的全局控制引擎为核心转换连接IPv4/IPv6网络并与特权虚拟机交互完成虚拟机在线迁移,可向客户端跨IPv4/IPv6网络提供云计算服务。该系统应用于IPv4/IPv6过渡期间云计算基础平台构建。     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。