基于CloudStack和OpenStack的KVM虚拟机跨平台迁移方法

在CloudStack平台与OpenStack平台共存的环境中,为了使CloudStack平台中已创建的KVM虚拟机在迁移到OpenStack平台后可以被OpenStack平台的控制节点正确识别并接管,提出了一种将CloudStack平台中已经存在的虚拟机动态迁移到OpenStack平台的方法。通过将传统基于本地存储的KVM虚拟机迁移方法与CloudStack以及OpenStack云计算平台自身特点相结合,重新对虚拟机迁移相关文件进行组合,实现了虚拟机跨平台的动态迁移。实验结果表明,本方法不但可以完成将KVM虚拟机成功从CloudStack平台迁移到OpenStack平台的任务,而且在时间上与传统方法相比并未产生其他时间成本。     

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.     

基于合作博弈的多虚拟机实时迁移带宽分配机制

IaaS云计算平台采用虚拟机实时迁移技术进行资源动态调度和管理。在实际应用场景下,需要并行实时迁移多个虚拟机。由于实时迁移算法本身以最大利用带宽的方式进行数据传输,存在着迁移进程间竞争带宽的问题,无法保证带宽全局最优分配,影响整体迁移的性能。提出一种基于合作博弈的多虚拟机实时迁移带宽分配机制,将带宽分配问题建模为一个纳什议价,通过求解纳什议价解得到帕累托最优的带宽分配方案,并在实际的虚拟化平台上进行了实现。实验结果表明,相比标准的并行实时迁移,所提出的带宽分配机制能够公平有效地分配带宽,提高了并行实时迁移的性能。     

基于用户个性化需求的虚拟机部署机制

在云环境中,虚拟机作为一个大粒度的资源,用户通常要求其运行具有稳定性,不希望出现虚拟机频繁迁移的现象。本文提出了一种基于用户个性化需求的虚拟机优化部署方案：根据用户请求的应用背景,在对四类资源（CPU、内存、硬盘和带宽）赋值后比较资源请求值和云平台中宿主机的可用资源值,选出候选目标主机,再根据目标函数计算选择出目标主机。该方案为用户选择较优的宿主机部署虚拟机,既可以科学合理地利用资源,有效地改善了负载不均衡现象,更能获得更好的用户体验。     

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。     

Symbiotic Dynamic Memory Balancing for Virtual Machines in Smart TV Systems

Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).     

计算机虚拟化技术的发展和应用

计算机虚拟化技术是IT领域中的热门话题之一，其作为一种强大的工具，被广泛应用于云计算、大数据、金融等领域。它通过抽象出硬件资源，将一台物理机的资源划分为多个虚拟机，提高了资源的利用率和灵活性，并显著降低了企业的生产成本。随着技术的不断发展，虚拟化技术正在不断演进和创新，未来的虚拟化技术将更高效、智能和安全。文中探究和分析了计算机虚拟化技术的发展和应用，以期读者能更好地了解虚拟化技术的价值和作用。     

Research on dynamic migration of virtual machine based on security level

Side-channel attacks were the main ways of multi-tenant information leakage in the cloud computing and data center environments.The existing defense approaches based on dynamic migration of virtual machine have long convergence time of migration algorithm and high migration cost.Hence,a dynamic migration of virtual machine based on security level was proposed.Firstly,security level classification of virtual machines was used to reduce the number of migrating virtual machines.Then the corresponding virtual machines embedding strategy was used to reduce the frequency of virtual machines migration.Simulation experiments demonstrate that the proposed approach can reduce convergence time of migration algorithm and migration cost.     

异构云DCN中绿色虚拟算法设计

为进一步提升异构云数据中心网络(DCN)动态管理的科学性,在总结当前主流研究局限性的基础上构思一种基于全局相对最优化的绿色虚拟算法.算法综合考虑虚拟机迁移过程中可能涉及到的诸多客观因素,通过科学地规划时间门限、主机筛选策略、以及精度比较机制对虚拟机实施高效的迁移.数据考察表明,所部署的算法不仅可快速精确地物色到最适宜的...     

Machine learning based optimized live virtual machine migration over WAN links

Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %.     

基于改进混合遗传算法的云资源调度算法

在云计算中,系统规模和虚拟机迁移数量都是十分庞大的,需要高效的调度策略对其进行优化。将云计算的任务分配抽象为背包求解问题,可通过遗传算法进行求解。传统的遗传算法具有局部搜索能力差以及早熟现象的缺点,本文采用遗传和贪婪相结合的混合遗传算法。针对混合遗传算法在资源利用率与能源消耗的收敛速度较慢问题,本文通过改进适应度函数,改变了适应度函数在不同染色体间的差异度,从而提高了染色体在选择算子中的择优性能。仿真结果表明,该方法能够有效提高混合遗传算法在云计算资源优化中的收敛速度。     

云环境下基于虚拟机动态迁移的调度策略研究

针对目前云环境资源调度采用静态负载均衡策略易于导致资源浪费的问题,提出了一种双限定值的虚拟机动态迁移的调度策略.该策略将当前负载状况与负载过重或过轻时两个限定值比较,选择介于二者之间能耗较低的虚拟机迁移至目标节点.仿真实验表明,该策略能够减少迁移次数,降低虚拟机迁移能耗,从而尽可能达到负载均衡和满足服务等级协议的需求.     

VM migration algorithm for the balance of energy resource across data centers in cloud computing

Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine(VM)migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.     

An Effective QoS-Constrained Scheduling Scheme for Cloud Computing Services

Cloud computing is becoming a hot topic of the information industry in recent years. Many companies provide the cloud services, such as Google Apps and Apple multimedia services. In general, by applying the virtualization technologies, the data center is built for cloud computing to provide users with the computing and storage resources, as well as the software environment. Thus, the quality of service (QoS) must be considered to satisfy users' requirements. This paper proposes a high efficiency scheduling scheme for supporting cloud computing. The virtual machine migration technique has been applied to the proposed scheduling scheme for improving the resources utilization and satisfying the QoS requirement of users. The experimental results show that in addition to satisfying the QoS requirement of users, the proposed scheme can improve the resources utilization effectively.     

Resource-efficient load-balancing framework for cloud data center networks

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.     

应用虚拟化技术实现云端服务模式的办公环境

虚拟化技术是计算机技术的基础之一,其应用范围非常广。近年来,网络信息技术和计算机硬件资源发展速度越来越快,进一步促进了虚拟技术的发展,使其得到了社会各界人士的广泛关注。虚拟及管理器(Virtual Machine Monitot,VMN)将物理机上的应用程序转移到了虚拟机(Virtual Machine,VM)上,优化了虚拟机资源调度和服务,提升了硬件资源使用效率,降低了运营成本。文中基于虚拟化技术开发了云办公环境,以期构建完善的新时期办公基础架构环境。     

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.     

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine （VM） is becoming a major issue for cloud providers. The existing virtual machine placement （VMP） solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization （MLU）. On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.     

移动IPv4/IPv6虚拟机迁移系统设计

为充分利用异构网络资源建设云计算基础平台,满足云计算平台的资源需求,文中设计了一种基于隧道技术、前缀管理、地址池管理协作及移动IP的IPv4/IPv6虚拟机迁移过渡框架.框架将传统IPv4/IPv6过渡技术与移动IP技术应用于云计算平台的虚拟机迁移,利用过渡控制引擎作为核心与开发的IPv4/IPv6插件交互完成虚拟机迁移.经实验验证框架建立的网络结构可向客户端跨IPv4/IPv6网络提供云计算服务适用于IPv4虚拟机与IPv6虚拟机之间的无缝迁移.该框架可应用于IPv4/IPv6过渡期间云计算基础平台建设.     

云环境下基于迁移的虚拟机集群优化算法

针对云环境中虚拟机集群负载不均衡问题,提出一种基于虚拟机迁移的集群优化算法。通过对节点负载的实时监测,动态调整各种资源的权重,根据资源权重选择可最大程度降低主机负载的虚拟机进行迁移。该算法利用预测机制,消除主机资源利用率的临时越界引起的不必要的虚拟机迁移。在选择目标节点时,采用多目标决策法,兼顾多资源匹配率,服务级目标违背率(SLA)等多种管理目标。实验结果表明,与同类型的负载均衡算法相比,该算法能减少迁移次数,降低SLA违背率。