QoS约束的虚拟机镜像放置优化方法

随着云计算的兴起, 虚拟机正逐步成为应用服务的部署环境, 如何高效、经济地管理虚拟机镜像文件至关重要. 提出了一种QoS约束的虚拟机镜像放置优化方法, 通过存储收益模型量化分析用户服务质量约束下的存储服务综合收益, 优化镜像文件的存储策略. 实验结果表明该方法可以在满足用户服务质量的前提下, 显著优化存储收益.     

面向多虚拟机的分布式存储系统

通过分析虚拟桌面系统对存储系统的要求,设计并实现了面向多虚拟机的分布式存储系统——虚拟机存储系统(virtual machine storage,VMStore)。分布式技术具有灵活性和可扩展性,因此采用分布式技术提高多虚拟机存储系统的吞吐率。在元数据管理中采用直接块索引结构以获得更好的快照性能,并采用去冗余技术减少多虚拟机镜像之间的冗余数据。实验结果显示存储系统具有较好的吞吐能力,且快照和去冗余的开销对整个系统的影响较小。     

修改客户操作系统优化KVM虚拟机的I/O性能

目前,运行在虚拟机上的客户操作系统(Guest OS)是面向物理机器开发的普通操作系统,其中存在不适应虚拟化环境的因素,影响虚拟机的I/O性能.作者通过测试发现了影响虚拟机I/O性能的一些问题,针对这些问题提出了优化方法:一方面,通过合并客户操作系统中连续的I/O指令,降低虚拟机的时钟中断频率,从而降低环境切换的开销;另一方面,消除客户操作系统中的冗余操作,包括在虚拟化环境下无效的函数、冗余的I/O调度以及虚拟网卡驱动对NAPI的支持,使虚拟机只执行必要的操作,从而提高系统的性能.     

虚拟机的可定制生成及其动态优化

本文针对虚拟机传统实现方法所存在的缺点，提出了一种虚拟机描述文件动态生成虚拟机的方法，给出了相应虚拟机的可定制生成模式及其对虚拟机动态优化的具体方案。该方案通过对特定虚拟机按规定语法格式进行描述形成一个虚拟机定义文件，并由虚拟机生成程序对其进行解析从而动态生成相应的虚拟机；针对特定的虚拟机程序，进一步利用动态优化的方法，对相对冗余的虚拟机指令集定义进行裁剪，从而得到一个指令集最简的高效虚拟机。     

一种用于虚拟集群的镜像按需分发框架

虚拟集群部署过程中,大量的镜像数据传输导致启动时间过长,可扩展性和效率受到影响。提出了镜像按需分发框架,根据虚拟机启动和运行过程中只对有限数据块访问的特性,仅传输虚拟机启动和运行所需数据块,避免了存储节点和网络瓶颈问题。进一步,提出了差异化的数据块划分方法,兼顾了镜像读写性能和分发效率：去冗余处理采用大数据块,提升管理效率和存取性能;采用小数据块划分启动区数据,提高传输效率。对于50个镜像的分发实验表明,相比于全镜像分发,按需分发速度可以提升13~22倍。     

VMware Workstation虚拟机镜像取证技术研究

云存储数据取证是电子数据取证领域的一个研究热点,虚拟机镜像文件是云存储虚拟化的一种载体。文章以VMware虚拟机镜像为研究对象,通过对镜像文件分析和比较,深入了解虚拟机镜像文件的结构组成和数据管理方式,提出一种建立数据链表、利用递归和回溯,实现读取虚拟机镜像数据的方法,解决了虚拟机镜像的读取问题,对云存储数据的提取和分析具有一定意义。     

基于BCDM的冗余时间变元绑定

基于双时态概念数据模型(BCDM)定义了BCDM中的冗余时间变元,对冗余进行了量化描述,提出一种对时间变元的去冗余绑定思想.介绍了BCDM,并对BCDM中的时间标签进行规范化的组合表达;形式地描述了时间变元在当前事务时间的去冗余绑定操作,并对双时态关系的去冗余绑定操作给出了形式描述;对BCDM中的时间变元的去冗余操作给出了实现算法,并进行了算法分析.     

基于软件注入的按需定制虚拟机实例系统

在云计算环境中,通常需要管理众多的计算资源,每个计算资源都因用户的需求不同而包含不同软件,造成了资源管理上的繁琐以及存储资源的浪费。通过对开源云计算平台及软件发布模式的研究,针对目前开源云平台下虚拟机管理系统的不足,文中设计了基于软件注入的按需定制虚拟机框架OpenAppV,并在开源云平台OpenStack下,实现了系统原型,对其关键部分软件注入进行了性能测试。测试结果表明,软件通过注入与虚拟机镜像来按需组合具备可行性,有效地解决了由于包含不同应用而形成的多个镜像所导致的存储与管理繁琐问题。     

计算机机房管理中的虚拟机技术

通过在计算机上安装虚拟机软件Virtual PC,使用这种虚拟机技术,可以消除学生上机实习操作内容多样性和机房安全管理稳定性之间的矛盾,不需要再对学生上机进行过多的限制,给学生营造一个宽松的学习环境,从而提高了学生计算机操作和应用能力.虚拟机技术值得在各类学校计算机教学实习和机房管理中大力推广使用.     

VM内部隔离驱动程序的可靠性架构

利用虚拟化技术来整合资源已成为高性能服务器提高资源利用率的重要手段,虚拟化技术的可靠性对于高性能服务器所提供服务的质量至关重要.然而,驱动故障严重影响了虚拟机中操作系统的可靠性,也同样影响到整个服务器的可靠性.为此,提出一种在虚拟机内部通过隔离故障驱动程序来提高虚拟机可靠性的架构,该架构通过监视驱动程序所使用的内存信息来建立驱动可写权限的授权表,并在虚拟机监视器中设置虚拟机内核空间对应影子页表的写保护来捕获虚拟机的写操作,进而结合授权表判断被隔离驱动程序写操作的正确性.目前,该架构能够在无需修改驱动程序的情况下,在虚拟机内部实现对驱动程序的隔离.实验结果表明：该架构可以隔离84.63%的注入故障造成的系统崩溃失效,并且对于驱动性能的影响小于20%,提高了虚拟化环境的可靠性.     

Efficient image restoration of virtual machines with reference count based rewriting and caching

Virtual machine (VM) image backups have duplicate data blocks distributed in different physical addresses, which occupy a large amount of storage space in a cloud computing platform (Choo et al.,  [1] and González-Manzano et al.,  [2]). Deduplication is a widely used technology to reduce the redundant data in a VM backup process. However, deduplication always causes the fragmentation of data blocks, which seriously affects the VM restoration performance. Current approaches often rewrite data blocks to accelerate image restoration, but rewriting could cause significant performance overhead because of frequent I/O operations. To address this issue, we have found that the reference count is a key to the fragmentation degree from a series of experiments. Thus, we propose a reference count based rewriting method to defragment VM image backups, and a caching method based on the distribution of rewritten data blocks to restore VM images. Compared with existing studies, our approach has no interfere to the deduplication process, needs no extra storage, and efficiently improves the performance of VM image restoration. We have implemented a prototype to evaluate our approach in our real cloud computing platform OnceCloud. Experimental results show that our approach can reduce about 57% of the dispersion degree of data blocks, and accelerate about 51% of the image restoration of virtual machines.     

基于重复数据删除的虚拟桌面存储优化技术

虚拟桌面基础架构依靠数据中心海量的云基础设施,为用户按需提供虚拟桌面部署所需的软硬件资源,但同时面临存储资源利用率低和虚拟机启动慢的困境.针对虚拟桌面存储中具有大量数据冗余的特性,采用重复数据删除技术缩减虚拟桌面基础架构的存储空间需求;并利用服务器本地磁盘缓存以及共享存储池内的固态硬盘来优化虚拟机的启动性能.通过原型实现,发现相比于基于内容分块的策略,静态分块策略更适合虚拟桌面存储进行重复数据删除,最优的分块大小为4KB,并能够缩减85%的存储空间容量;通过服务器本地磁盘缓存和基于闪存的固态硬盘进行I/O优化,虚拟机的启动速度能够获得35%的提升.     

面向托管的数据库即服务系统及资源优化技术

数据库即服务（DBaaS）是云计算的一个研究热点,而数据应用托管则是当前DBaaS的一个重要应用领域。为满足行业数据应用托管中对DBaaS提出的数据隔离、性能隔离及可靠性保障等方面的要求,提出一种无共享架构下基于虚拟机、支持副本的多租户数据托管方法及相应的数据库即服务系统。针对该系统中面向租户的虚拟机资源（CPU、内存等）动态优化这一核心问题,建立了基于虚拟机的系统资源效用函数和数据库性能计算模型,并在基础上给出了一种根据租户数据请求负载并采用贪心方式的虚拟机资源动态优化算法。结合科技信息服务数据库托管应用示例进行了实验,实验结果表明提出的方法可以根据各个租户的数据库负载动态优化虚拟机的资源分配,能够在满足性能需求同时达到了提高系统资源利用率的目的。     

Virtual machine consolidation based on interference modeling

Server consolidation is very attractive for cloud computing platforms to improve energy efficiency and resource utilization. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server. However, current virtualization technologies do not ensure performance isolation among guest virtual machines, which results in degraded performance due to contention in shared resources along with violation of service level agreement (SLA) of the cloud service. In that sense, minimizing performance interference among co-located virtual machines is the key factor of successful server consolidation policy in the cloud computing platforms. In this work, we propose a performance model that considers interferences in the shared last-level cache and memory bus. Our performance interference model can estimate how much an application will hurt others and how much an application will suffer from others. We also present a virtual machine consolidation method called swim which is based on our interference model. Experimental results show that the average performance degradation ratio by swim is comparable to the optimal allocation.     

A secure cloud storage system supporting privacy-preserving fuzzy deduplication

Deduplication is an important technology in the cloud storage service. For protecting user privacy, sensitive data usually have to be encrypted before outsourcing. This makes secure data deduplication a challenging task. Although convergent encryption is used to securely eliminate duplicate copies on the encrypted data, these secure deduplication techniques support only exact data deduplication. That is, there is no tolerance of differences in traditional deduplication schemes. This requirement is too strict for multimedia data including image. For images, typical modifications such as resizing and compression only change their binary presentation but maintain human visual perceptions, which should be eliminated as duplicate copies. Those perceptual similar images occupy a lot of storage space on the remote server and greatly affect the efficiency of deduplication system. In this paper, we first formalize and solve the problem of effective fuzzy image deduplication while maintaining user privacy. Our solution eliminates duplicated images based on the measurement of image similarity over encrypted data. The robustness evaluation is given and demonstrates that this fuzzy deduplication system is able to duplicate perceptual similar images, which optimizes the storage and bandwidth overhead greatly in cloud storage service.     

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。     

云数据中心能效可靠的虚拟机部署

优化虚拟机部署是降低云数据中心能耗的有效方法,但是,过度对虚拟机部署进行合并可能导致主机机架出现热点,影响数据中心提供服务的可靠性。提出一种基于能效和可靠性的虚拟机部署算法。综合考虑主机利用率、主机温度、主机功耗、冷却系统功耗和主机可靠性间的相互关系,建立确保主机可靠性的冗余模型。在主动避免机架热点情况下,实现动态的虚拟机部署决策,在降低数据中心总体能耗前提下,确保主机服务可靠性。仿真结果表明,该算法不仅可以节省更多能耗,避免热点主机,而且性能保障上也更好。     

申威架构下的虚拟机访存特征提取方法

虚拟化技术是云服务的重要支柱之一,虚拟化充分扩展了物理资源的灵活性,提升了物理资源的利用率。随着国家信息化水平的发展,云服务器核心技术自主可控、安全高效的要求不断提高。近年来,作为国产服务器的典型代表,申威架构服务器的功能不断完善。提出了申威架构上的虚拟机访存特征提取方法,充分利用了申威架构独特优势,实时测算虚拟机的内存缺失率曲线,并最终计算工作集大小,同时利用热页集机制大幅度减少页面追踪的性能开销。实验结果表明,该方法可以准确计算虚拟机工作集大小,平均误差低于3%,平均性能开销不高于8.3%。本工作为申威虚拟机内存动态分配提供条件,最终目标是提高申威云服务器整体性能和内存利用率。     

A security-aware virtual machine placement in the cloud using hesitant fuzzy decision-making processes

The introduction of cloud computing systems brought with itself a solution for the dynamic scaling of computing resources leveraging various approaches for providing computing power, networking, and storage. On the other hand, it helped decrease the human resource cost by delegating the maintenance cost of infrastructures and platforms to the cloud providers. Nevertheless, the security risks of utilizing shared resources are recognized as one of the major concerns in using cloud computing environments. To be more specific, an intruder can attack a virtual machine and consequently extend his/her attack to other virtual machines that are co-located on the same physical machine. The worst situation is when the hypervisor is compromised in which all the virtual machines assigned to the physical node will be under security risk. To address these issues, we have proposed a security-aware virtual machine placement scheme to reduce the risk of co-location for vulnerable virtual machines. Four attributes are introduced to reduce the aforementioned risk including the vulnerability level of a virtual machine, the importance level of a virtual machine in the given context, the cumulative vulnerability level of a physical machine, and the capacity of a physical machine for the allocation of new virtual machines. Nevertheless, the evaluation of security risks, due to the various vulnerabilities’ nature as well as the different properties of deployment environments is not quite accurate. To manage the precision of security evaluations, it is vital to consider hesitancy factors regarding security evaluations. To consider hesitancy in the proposed method, hesitant fuzzy sets are used. In the proposed method, the priorities of the cloud provider for the allocation of virtual machines are also considered. This will allow the model to assign more weights to attributes that have higher importance for the cloud provider. Eventually, the simulation results for the devised scenarios demonstrate that the proposed method can reduce the overall security risk of the given cloud data center. The results show that the proposed approach can reduce the risk of attacks caused by the co-location of virtual machines up to 41% compared to the existing approaches.