基于虚拟机迁移的数据中心异构负载的能耗优化算法

随着虚拟化技术在数据中心的应用,虚拟机不仅被用作整合未充分使用的节点和节省能源的工具,还允许为各类异构的负载提供封装管理.本文将Web的应用程序或各类计算作业封装在虚拟机中,并将其视为一个可以更有利于管理的实例进行资源的调度.提出一种新的虚拟化数据中心的调度策略,主要是在优化数据中心成本的同时,根据多个方面对数据中心节点中的虚拟机进行分配.本文提出的算法综合考虑了能源效率、虚拟化开销、冗余性和服务等级,通过运行综合成本指标得分解决数据中心能耗问题.     

云计算虚拟化技术研究

本文对云计算虚拟化技术的概念及其发展现状进行了阐述,并对虚拟化技术在云计算中的应用进行了具体分析。虚拟化技术包括虚拟整合、虚拟拆分和虚拟迁移。云计算使用虚拟整合技术将云平台中异构的物理资源整合成一个资源池,方便对资源的管理和分配;虚拟拆分将每台服务器拆分为多个虚拟机,通过虚拟桌面技术,每个用户能独占一个虚拟机,运行自己的应用,互不影响;虚拟迁移技术能够帮助云平台进行负载均衡和节约运行成本。最后对几种虚拟软件的性能进行了测试对比,分析了虚拟化带来的资源损耗问题。     

数据中心虚拟化环境的安全挑战

云计算给网络安全带来新的挑战,网络安全策略要跟随虚拟机的迁移而迁移,部署云计算环境下的网络安全需要引入新思路和新技术,如基于VXLAN的VLAN扩展、云防火墙等. 虚拟化提升安全复杂度 网络安全是数据中心建设的重要组成部分,是数据中心内部业务健壮运行的基础保障.虚拟化使得数据中心内IT架构复杂性明显提高,业务部署由传统的物理机、单服务器单应用、静态模式在向服务器虚拟化、单服务器多应用、动态模式演进.网络也深入到了服务器虚拟化层,为了保持虚拟化环境与物理机环境具有相同的网络功能、策略、安全,这也就增加了网络部署与管理的复杂度.     

基于工作负载预测的虚拟机整合算法

针对云计算环境下满足负载均衡、自动伸缩、绿色节能等需求时所面临的虚拟机(VM)迁移问题,该文设计一种面向云计算基础设施基于工作负载预测的整合调度算法。通过有机结合基于工作负载预测的主动控制技术和基于实际系统状态信息的被动控制技术,并采用指数平滑预测模型预测未来时刻的工作负载情况,提出虚拟机选择阶段最大未来工作负载优先和虚拟机安置阶段比较资源需求队列的虚拟机整合算法。仿真表明,该算法利用基于预测的资源整合方式减少了服务器使用量、虚拟机迁移次数和服务等级协议违例次数,有效提升了以数据中心为核心的云基础设施整体资源利用率。     

云计算数据中心可扩展服务器的能耗管理与优化

通过对云计算数据中心可扩展服务器功耗管理相关研究的分析,设计了云计算能耗优化管理的架构,并提出数据中心服务器节能优化机制流程图,在广泛支持的DVFS技术能耗特性基础上,提出效能优化策略,通过转移高负载,经转化高负载的主机成为低负载的主机,实现云计算数据中心可扩展服务器能耗管理的优化。采用内存为12 GB的12台主机和1台处理能力为3 200 MIPS的处理器组成数据中心进行仿真实验,结果表明,提出的效能优化策略比无迁移策略节省能耗约24.33%,比DVFS策略节省能耗约15.23%,且综合性能要优于其他两个策略,采用提出的策略可进行云计算数据中心扩展服务器能耗使用的优化。     

桌面虚拟化在集中监控组网中的应用

桌面虚拟化技术是服务器虚拟化技术的延伸应用.服务器虚拟化,即操作系统、应用程序及与服务器工作负荷有关的I/O封装在一个名为虚拟机的文件中,让多个虚拟机在一台服务器上运行.因此,可以将桌面系统映像存放在数据中心的服务器统一部署和管理.采用桌面虚拟化技术可以解决集中监控中心的环境、组网及效率问题.从集中监控中心的情况入手,...     

高性能、高可靠中兴超融合架构关键技术解析

在传统IT架构向云计算迁移过程中,服务器虚拟化是第一步,但仅有服务器虚拟化绝对不能算是云计算.另外,即使在一个中小企业的数据中心内,也有来自不同厂家的虚拟化软件、服务器和存储产品,用户渴望更专业的服务来“拼积木”,在服务器上实现全部融合虚拟化计算及存储,以减少采购成本和能耗开销,降低安装、管理和运维的难度.     

绿色数据中心的服务器节能机制与策略

从数据中心的能效评价和能耗组成入手,说明了服务器节能在数据中心节能中的重要性,介绍了服务器的节能机制和策略,包括处理器动态调节、电源管理和虚拟机动态调度等,接着比较了不同层面的节能策略,对目前主要的集成节能方案进行了小结,最后阐述了数据中心服务器节能研究和应用上面临的挑战.     

云数据中心的能耗管理研究

不断增长的能源消耗已经成为制约云计算发展的瓶颈。分析了云数据中心的能耗分布,指出了降低服务器集群能耗的重要性。在对目前服务器集群能耗模型做总结的基础上,从虚拟化、任务调度、负载均衡以及DVFS四个方面论述了目前云数据中心节能技术、策略及其相应的算法。     

数据中心服务器负载均衡问题研究

数据中心服务器的存储容量、处理能力、访问速度和忙闲不均等问题制约了数据中心服务能力提升。服务器负载均衡技术为解决这些问题提供了途径。文章介绍服务器负载均衡运行机制,即利用服务器健康性检测功能,根据系统资源利用率和服务类型确定负载均衡类型,设计负载均衡算法。分析软件、硬件和虚拟化技术负载均衡解决方案,并提出三者融合高质量实现数据中心服务器负载均衡的设计理念。     

Correlation-based virtual machine migration in dynamic cloud environments

Virtual machine (VM) migration enables flexible and efficient resource management in modern data centers. Although various VM migration algorithms have been proposed to improve the utilization of physical resources in data centers, they generally focus on how to select VMs to be migrated only according to their resource requirements and ignore the relationship between the VMs and servers with respect to their varying resource usage as well as the time at which the VMs should be migrated. This may dramatically degrade the algorithm performance and increase the operating and the capital cost when the resource requirements of the VMs change dynamically over time. In this paper, we propose an integrated VM migration strategy to jointly consider and address these issues. First, we establish a service level agreement-based soft migration mechanism to significantly reduce the number of VM migrations. Then, we develop two algorithms to solve the VM and server selection issues, in which the correlation between the VMs and the servers is used to identify the appropriate VMs to be migrated and the destination servers for them. The experimental results obtained from extensive simulations show the effectiveness of the proposed algorithms compared to traditional schemes in terms of the rate of resource usage, the operating cost and the capital cost.     

Burst‐aware virtual machine migration for improving performance in the cloud

Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst‐aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.     

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Cloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state‐of‐the‐art multiobjective VM placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.     

A learning‐based approach for virtual machine placement in cloud data centers

In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms.     

云计算环境下资源评级的虚拟机部署算法

针对虚拟机(VM)大范围集群部署时容易引发数据中心(DC)总体负载不均衡、VM部署拒绝率居高不下等问题。提出一种基于资源评级的VM部署算法,根据构建VNF的VM之间的亲和关系、物理机之间的拓扑关系及链路的带宽,选择出最适合部署VNF的一台或多台物理机(PM)。仿真结果表明,在资源相同的情况下,该算法较已有算法具有更高的资源均衡度和更低的部署拒绝率。     

PAVM: a framework for policy‐aware virtual machine management

The problem of efficient placement of virtual machines (VMs) in cloud computing infrastructure is well studied in the literature. VM placement decision involves selecting a physical machine in the data center to host a specific VM. This decision could play a pivotal role in yielding high efficiency for both the cloud and its users. Also, reallocation of VMs could be performed through migrations to achieve goals like higher server consolidation or power saving. VM placement and reallocation decisions may consider affinities such as memory sharing, CPU processing, disk sharing, and network bandwidth requirements between VMs defined in multiple dimensions. Considering the NP‐hard complexity associated with computing an optimal solution for this VM placement decision problem, existing research employs heuristic‐based techniques to compute an efficient solution. However, most of these approaches are restricted to only a single attribute at a time. That is, a given technique of using heuristics to compute VM placement considers only a single attribute, while completely ignoring the impact of other dimensions of placing VMs. While this approach may improve the efficiency with respect to the affinity attribute in consideration, it may yield degraded performance with respect to other affinities. In addition, the criteria for determining VM‐placement efficiency may vary for different applications. Hence, the overall goal of achieving VM placement efficiency becomes difficult and challenging. We are motivated by this challenging problem of efficient VM placement and propose policy‐aware virtual machine management (PAVM), a generic framework that can be used for efficient VM management in a cloud computing platform based on the service provider‐defined policies to achieve the desired system‐wide goals. This involves efficient means to profile different VM affinities and to use profiled information effectively by intelligent and efficient VM migrations at run time considering multiple attributes at a time. By conducting extensive evaluation through simulation and real experiments that involve VM affinities on the basis of network and memory, we confirmed that the PAVM architecture is capable of improving the efficiency of a cloud system. We elaborate the architecture of a PAVM system, describe its implementation, and present details of our experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

TRACTOR: Traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization

Technology providers heavily exploit the usage of edge-cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network-traffic effectiveness. In this study, we present a multi-objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR , which utilizes an artificial bee colony optimization algorithm for power and network-aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three-tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters.     

Resource allocation in cloud virtual machines based on empirical service traces

One of the key technologies in cloud computing is virtualization. Using virtualization, a system can optimize usage of resources, simplify management of infrastructure and software, and reduce hardware requirements. This research focuses on infrastructure as a service, resource allocation by providers for consumers, and explores the optimization of system utilization based on actual service traces of a real world cloud computing site. Before activating additional virtual machines (VM) for applications, the system examines CPU usage in the resource pools. The behavior of each VM can be estimated by monitoring the CPU usage for different types of services, and consequently, additional resources added or idle resources released. Based on historical observations of the required resources for each kind of service, the system can efficiently dispatch VMs. The proposed scheme can efficiently and effectively distribute resources to VMs for maximizing utilization of the cloud computing center. Copyright © 2013 John Wiley & Sons, Ltd.     

Fault tolerance mechanisms for virtual data center architectures

A virtual data center (VDC) is a combination of interconnected virtual servers hosted on a physical data center that hosts multiple such VDCs. This enables efficient sharing of the data center’s resources while handling dynamic resource requirements of the clients. The SecondNet architecture (Guo et al. in Proceedings of ACMSIGCOMM conference on data communication, Barcelona, pp 63–74, 2009) realizes this VDC concept and includes a centralized VDC resource-mapping (virtual to physical) algorithm. Fault tolerance is an important requirement in data center-based services, in order to increase reliability and availability. In this paper, we propose a fault tolerance mechanism to handle server failures by efficiently migrating the virtual machines (VMs) hosted on the failed server to a new location. Using our mechanism, it is shown that recovery from all the faults is possible, even for a server utilization of 90 %. In order to reduce the impact of server failures on the VDCs hosted in the data center, we then present a new load balancing scheme based on clustering that efficiently allocates the VDCs on the data center. Using this scheme, we were able to reduce the affected number of VMs per server failure by 63 %, in case of a BCube network of size 625 nodes, and by 86 %, in case of a BCube network of size 1,296 nodes.