云计算平台中多虚拟机内存协同优化策略研究

虚拟化技术为云计算基础设施资源的动态部署、安全隔离提供了重要保证.从过度占用内存的虚拟机中回收内存,提供给内存紧缺的虚拟机使用,优化多虚拟机间的内存分布是内存虚拟化中的挑战性问题.文中引入了自发调节和全局调节协作的多虚拟机内存管理架构.通过定义内存资源充裕状态和内存资源紧缺状态,提出了自发调节和全局调节之间协作的算法....     

用VMware构建高效的网络安全实验床*

采用几个新的技术来管理VMware内存。用内存虚拟术回收页面使得运行在虚拟机上的操作系统占用最少的内存。设计基于内容的页面共享技术来消除冗余内存。综合利用这些技术可高效地支持虚拟机系统对过量使用内存。     

IaaS模式下虚拟机部署机制研究

针对将基础设施作为服务的云计算平台中虚拟机部署问题,通过分析现有云计算平台的应用模式,建立了虚拟机部署模型,提出了一种快速资源匹配策略.该模型根据计算节点可用CPU个数和内存的大小将其划分到对应的二维容器队列中,然后再进行资源的匹配.在进行资源匹配时采用了计算单元优先匹配的策略,提高了计算节点CPU的利用率.将文中提出的虚拟机部署机制在基于Eucalyptus的开源云计算平台中实现.实验测试表明,该虚拟机部署机制实现了虚拟机的快速部署,提高了资源的利用率和部署效率.     

PAMM:一种面向基于内存共享的域间通信的优化模型

云计算平台和虚拟化技术的结合为虚拟机域间通信带来了新的需求,基于内存共享的域间通信可以提高运行在同一物理机上的虚拟机间的通信效率。但是,基于内存共享的域间过程中产生的上下文状态切换限制了其优化能力。引入一种新的内存共享模型PAMM,即通过添加一个管理模块对内存共享过程中所传递的内存页进行聚合管理,减少申请超级调用的次数,以达到减少状态切换的目的。实验表明,PAMM能够提升基于内存共享的域间通信的通信效率。     

基于虚拟化的云计算平台的内存管理研究

虚拟化技术使云计算能够统一管理计算资源、提高资源利用率。主要研究虚拟机的内存虚拟化方式以及虚拟机内存调整技术。虚拟机内存资源在灵活的调制机制下可弹性变化,为提高云计算的扩展性提供重要的技术支持,提高系统整体性能。     

虚拟机中的编译优化技术

传统静态编译技术给虚拟机的性能带来了极大的挑战：首先,可移植的程序表示以及动态语言的特性（例如动态类加载）迫使多数优化都推迟到运行时进行,导致运行时增加了优化开销;第二,模块化设计的程序使得很多针对整个程序的过程间优化技术难以应用;最后,虚拟机在一些运行间服务上（如安全机制和自动内存管理）花费了大量的资源。为了解决这些问题,商品化的虚拟机厂商以及一些研究机构都花费了大量时间和精力在虚拟机上研究开发自适应优化系统。当前的主流虚拟机基本都包含了一些基层实现以便于来实现实时程序分析、动态编译以及基于反馈的动态优化技术。介绍了虚拟机中的编译优化技术的产生发展以及当前虚拟机中流行的动态优化技术。     

云计算虚拟化技术的发展与趋势

云计算是一种融合了多项计算机技术的以数据和处理能力为中心的密集型计算模式,其中以虚拟化、分布式数据存储、分布式并发编程模型、大规模数据管理和分布式资源管理技术最为关键。经过十多年的发展,云计算技术已经从发展培育期步入快速成长期,越来越多的企业已经开始使用云计算服务。与此同时,云计算的核心技术也在发生着巨大的变化,新一代的技术正在改进甚至取代前一代技术。容器虚拟化技术以其轻便、灵活和快速部署等特性对传统的基于虚拟机的虚拟化技术带来了颠覆性的挑战,正在改变着基础设施即服务（IaaS）平台和平台即服务（PaaS）平台的架构和实现。对容器虚拟化技术进行深入介绍,并通过分析和比较阐述容器虚拟化技术和虚拟机虚拟化技术各自的优势、适应场景和亟待解决的问题,然后对云计算虚拟化技术的下一步研究方向和发展趋势进行展望。     

Hyper-V来宾可以使用传递磁盘并仍然使用实时迁移技术吗？

实时迁移是一种技术,它可以在运行来宾的同时向目标节点复制正在运行的Hyper—V来宾内存,这意味着从一个Hyper—V主机向另一个Hyper-V主机传递虚拟机（VM）状态时,来宾不再需要停机时间。为了解决卸载和装载LUN（用于存储VM的VHD文件）操作的延迟,系统需要使用群集共享卷（CSV）,CSV允许群集中的所有节点同时访问启用了CSV的存储器,因此不需要执行卸载／装载操作。     

云计算中基于拍卖的虚拟机动态供应和分配算法

当前云计算供应商通过定价算法或类似拍卖的算法来分配他们的虚拟机(VM)实例。然而,这些算法大多要求虚拟机静态供应,无法准确预测用户需求,导致资源未得到充分利用。为此,提出了一种基于组合拍卖的虚拟机动态供应和分配算法,在做出虚拟机供应决策时考虑用户对虚拟机的需求。该算法将可用的计算资源看成是“流体”资源,且这些资源根据用户请求可分为不同数量、不同类型的虚拟机实例。然后可根据用户的估价决定分配策略,直到所有资源分配完毕。基于并行工作负载存档(Parallel Workload Archive)的真实工作负载数据进行了仿真实验,仿真结果表明所提方法可保证为云供应商带来更高收入,提高资源利用率。     

基于VMI技术的内存泄露的检测

文中用VMI（虚拟机自省）技术实现了由外部对VM（虚拟机）内部内存大小的透明实时的监控。VMI技术在纯净的环境下记录虚拟机的内存量,用增量叠加的方法记录加入此域的内存泄露的程序对内存的影响。内存泄漏的堆积会最终消耗尽系统所有内存,因此内存泄漏是造成计算机安全事故的主要原因之一。本文实验通过开放源代码虚拟机监视器Xen,使用了xen内核的半虚拟化模式,由外部检测虚拟机进程的内存消耗量来判断应用程序是否有内存泄露。由于这种方式可以为用户提供独立、隔离的计算环境,不需要修改源程序或者对程序进行重新编译,不必有其它特殊硬件需求,系统性能损失较小并且有一定的通用性,因而在判断内存泄露是其它解决方案无法取代的。实验结果表明：对内存的监控数据证明了实验系统的有效性和可行性。     

一种基于资源预分配的虚拟机软实时调度方法

虚拟机技术作为云计算的重要技术之一,近年来得到广泛关注,但是由于虚拟机管理层的存在,导致语义鸿沟,使得实时应用程序、并发程序等在虚拟机上的运行性能受到影响。分析和研究了Xen虚拟机管理器的Credit调度算法,针对其在并发调度和软实时调度方面存在的不足,提出了改进调度算法,实现了算法的调度器原型。新的调度算法对软实时虚拟机进行Credit比例预分配,采用动态调度时间片机制,以non-work-conserving方式实现软实时任务周期调度,保障调度周期满足运行周期要求。通过区分并发和非并发软实时虚拟机,采取不同的调度策略,在满足资源利用率的基础上,确保实时任务的顺利运行。测试结果表明,该调度算法在对并发和非并发软实时任务调度上,具有良好的表现,较好满足了软实时应用调度需求。     

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Cloud computing provides scalable computing and storage resources over the Internet. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. The required resources of a VM are sliced from a physical machine (PM) in the cloud computing system. A PM may hold one or more VMs. When a cloud provider would like to create a number of VMs, the main concerned issue is the VM placement problem, such that how to place these VMs at appropriate PMs to provision their required resources of VMs. However, if two or more VMs are placed at the same PM, there exists certain degree of interference between these VMs due to sharing non-sliceable resources, e.g. I/O resources. This phenomenon is called as the VM interference. The VM interference will affect the performance of applications running in VMs, especially the delay-sensitive applications. The delay-sensitive applications have quality of service (QoS) requirements in their data access delays. This paper investigates how to integrate QoS awareness with virtualization in cloud computing systems, such as the QoS-aware VM placement (QAVMP) problem. In addition to fully exploiting the resources of PMs, the QAVMP problem considers the QoS requirements of user applications and the VM interference reduction. Therefore, in the QAVMP problem, there are following three factors: resource utilization, application QoS, and VM interference. We first formulate the QAVMP problem as an Integer Linear Programming (ILP) model by integrating the three factors as the profit of cloud provider. Due to the computation complexity of the ILP model, we propose a polynomial-time heuristic algorithm to efficiently solve the QAVMP problem. In the heuristic algorithm, a bipartite graph is modeled to represent all the possible placement relationships between VMs and PMs. Then, the VMs are gradually placed at their preferable PMs to maximize the profit of cloud provider as much as possible. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.     

vSpec: workload-adaptive operating system specialization for virtual machines in cloud computing云计算环境下负载自适应的虚拟机操作系统定制化

In general, operating systems (OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same configuration. Applications in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine (VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, vSpec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. vSpec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of vSpec on benchmarks and real-world applications.     

支持随机服务请求的云虚拟机按需物理资源分配方法

本文针对云平台按负载峰值需求配置处理机资源、提供单一的服务应用和资源需求动态变化导致资源利用率低下的问题,采用云虚拟机中心来同时提供多种服务应用.利用灰色波形预测算法对未来时间段内到达虚拟机的服务请求量进行预测,给出兼顾资源需求和服务优先等级的虚拟机服务效用函数,以最大化物理机的服务效用值为目标,为物理机内的各虚拟机动态配置物理资源.通过同类虚拟机间的全局负载均衡和多次物理机内各虚拟机的物理资源再分配,进一步增加服务请求量较大的相应类型的虚拟机的物理资源分配量.最后,给出了虚拟机中心基于灰色波形预测的按需资源分配算法ODRGWF.模拟实验表明所提算法能够有效提高云平台中处理机的资源利用率,对提高用户请求完成率以及服务质量都具有实际意义.     

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.     

虚拟机中的通信机制的安全问题研究

自从1960年前后IBM成功推出VM/370,虚拟机的发展已经有了40多年的历史,尤其是最近几年VMware和Xen等针对X86架构的虚拟机产品的问世把对虚拟机的研究推到了工业界和研究界的前沿。人们在考虑虚拟机带来资源利用率提高的同时,也在关注着虚拟机的引入对计算机网络信息安全的影响。虚拟化能实现部分隔离功能,能增加系统的安全性。本文对完全硬件仿真虚拟机、半虚拟化虚拟机以及操作系统级虚拟机中的通讯机制的安全性进行分析,详细描述了目前的研究情况、存在的问题以及一些有针对性的优化方法。     

Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.     

Energy-efficiency enhanced virtual machine scheduling policy for mixed workloads in cloud environments

Virtualization technology is an effective approach to improving the energy-efficiency in cloud platforms; however, it also introduces many energy-efficiency losses especially when I/O virtualization is involved. In this paper, we present an energy-efficiency enhanced virtual machine (VM) scheduling policy, namely Share-Reclaiming with Collective I/O (SRC-I/O), with aiming at reducing the energy-efficiency losses caused by I/O virtualization. The proposed SRC-I/O scheduler allows VMs to reclaim extra CPU shares in certain conditions so as to increase CPU utilization. Meanwhile, it separates I/O-intensive VMs from CPU-intensive ones and schedules them in a collective manner, so as to reduce the context-switching cost when scheduling mixed workloads. Extensive experiments are conducted on various platforms to investigate the performance of the proposed scheduler. The results indicate that when the system is in presence of mixed workloads, SRC-I/O scheduler outperforms many existing VM schedulers in terms of energy-efficiency and I/O responsiveness.     

Utilization of workflow management system for virtual machine instance management on cloud

The concept of virtualization is one of the most important technologies to construct a cloud service, and especially hardware virtualization is indispensable for infrastructure as a service (IaaS) where the cloud offering, infrastructure, is usually provided as a pool of virtual machine (VM) instances. For that reason, many public IaaS clouds like Amazon Web Service and private cloud toolkits such as Eucalyptus and OpenStack provide users with methods for managing VM instances via APIs, command‐line tools, web services, and so on. These are, however, not easy to use or customize for the average end users, especially for those in scientific research areas who just want to perform their work on a cloud and do not need to know the underlying technologies that much. Utilizing workflow management systems (WfMSs) in managing VMs on a cloud can alleviate these difficulties. Users only need to describe parameters needed for VMs and enact the workflow on a workflow enactment engine using user‐friendly interfaces. We propose a management scheme for VM instances on a cloud with the WfMS in this paper. We present a preliminary study on integrating cloud and WfMS focusing on management of VM instances and show an early implementation for a proof of concept with detailed explanations and possible usage scenarios. Copyright © 2015 John Wiley & Sons, Ltd.