Mbalancer:虚拟机内存资源动态预测与调配

在现代数据中心,虚拟化技术在资源管理、服务器整合、提高资源利用率等方面发挥了巨大的作用,已成为云计算架构中关键的抽象层次和重要的支撑性技术。在虚拟化环境中,如果要保证高资源利用率和系统性能,必须有一个高效的内存管理方法,使得虚拟机的物理内存大小能够满足应用程序不断变化的内存需求。因此,如何在单机以及数据中心内进行内存资源的动态调控,就成为了一个关键性问题。实现了一个低开销、高精确度的内存工作集跟踪机制,进而进行相应的本地或者全局的内存调控。采用了多种动态内存调控技术：气球技术能够在单机内有效地为各个虚拟机动态调节内存;远程缓存技术可在物理机之间进行内存调度;虚拟机迁移可将虚拟机负载在多个物理主机间进行均衡。深入分析了以上各种方案的优缺点,并根据内存超载的情况有针对性地设计了相应的调控策略,实验数据表明：所提出的预测式的内存资源管理方法能够对内存资源进行在线监控和动态调配,并有效地提高了数据中心的内存资源利用率,降低了数据中心能耗。     

墓于MIPS架构的内存虚拟化研究

内存虚拟化是系统虚拟化中如何有效抽象、利用、隔离计算机物理内存的重要方法,决定着系统虚拟化的整体性能.传统的纯软件内存虚拟化方法会产生较大的资源开销并且兼容性差,而硬件辅助的内存虚拟化方法需要重新设计处理器硬件架构.基于MIPS架构处理器提出一种软硬件协同的内存虚拟化方法,在不增加硬件支持的情况下提高内存虚拟化性能.提出的多层虚拟地址空间模型不仅可以解决MIPS架构处理器存在的虚拟化缺陷,而且可以在已有的内存虚拟化方法上提高性能.在多层虚拟地址空间模型的基础上,提出基于地址空间标识码(address space identity,ASID)、动态划分的旁路转换缓冲(translation lookaside buffer,TLB)共享方法,降低了虚拟机切换的开销.最终,在MIPS架构的龙芯3号处理器上实现了系统虚拟机VIRT-LOONGSO)N.性能测试表明,提出的方法可以提高大多数测试程序的性能,达到二进制翻译执行性能的3～5倍,并在TLB模拟方法的基础上提高了5％～16％的性能.     

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。     

浅论服务器虚拟化

0前言将服务器的物理资源,通过虚拟技术变成多台可以相互隔离的虚拟服务器,不受限于物理上的界限,而是让CPU、内存、磁盘、I/O等硬件变成可以动态管理的共享资源。从而提高资源的利用率,简化系统管理,实现服务器整--这就是服务器的虚拟化。1服务器虚拟化的类型虚拟化管理所需的硬件设备为虚拟机监视器,软件是通过虚拟化平台进行管理。虚拟机监视器(Virtual Machine Monitor)把     

一种面向虚拟化云计算平台的内存优化技术

虚拟化技术和云计算平台的结合带来了全新的资源整合和使用模式,基于虚拟化技术的资源按需分配与调度可以提高云平台资源的利用率,提升云服务的服务质量,并降低云用户的总体拥有成本.但是,物理服务器的资源边界限制了资源的全局优化能力.文中引入了一种面向虚拟化云平台的全局内存优化框架-通过引入由逻辑地址空间和全局扩展地址空间构成的...     

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.     

基于虚拟化内存隔离的Rowhammer攻击防护机制

随着虚拟化技术的发展与云计算的流行,虚拟化环境下的安全防护问题一直受到广泛的关注。最近的Rowhammer攻击打破了人们对于硬件的信赖,同时基于Rowhammer攻击的各种攻击方式已经威胁到了虚拟化环境下的虚拟机监视器以及其他虚拟机的安全。目前业界已有的对Rowhammer攻击的防御机制或者局限于修改物理硬件,或者无法很好的部署在虚拟化环境下。本文提出一种方案,该方案实现了一套在虚拟机监视器层面的Rowhammer感知的内存分配机制,能够在虚拟机监视器层面以虚拟机的粒度进行Rowhammer攻击的隔离防护。测试表明,该方案能够在不修改硬件,以及引入较小的性能开销（小于6%的运行时开销和小于0.1%的内存开销）的前提下,成功阻止从虚拟机到虚拟机监视器以及跨虚拟机的Rowhammer攻击。     

基于EPT的内存虚拟化研究与实现

为降低虚拟机监控器在内存虚拟化方面的开销,提高内存虚拟化性能,分析了两种的内存虚拟化机制,着重对基于Intel扩展页表的内存虚拟化机制进行了研究,分析了基于展页表的两种内存虚拟化方案优劣,并进一步分析了影响内存虚拟化性能的因素.针对扩展页表页故障,提出了页池的动态内存分配方案.内存虚拟化实现表明,采用扩展页表实现内存虚拟化能简化了设计流程,有效地提高了内存虚拟化性能.     

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....     

双层虚拟化云架构下任务调度能耗优化算法

虚拟机上部署容器的双层虚拟化云架构在云数据中心中的使用越来越广泛。为了解决该架构下云数据中心的能耗问题,提出了一种工作流任务调度算法TUMS-RTC。针对有截止时间约束的并行工作流,算法将调度过程划分为时间利用率最大化调度和运行时间压缩两个阶段。时间利用率最大化调度通过充分使用给定的时间范围减少完成工作流所需的虚拟机和服务器数量;运行时间压缩阶段通过压缩虚拟机空闲时间以缩短虚拟机和服务器的工作时间,最终达到降低能耗的目标。使用大量特征可控的随机工作流对TUMS-RTC算法的性能进行了测试。实验结果表明,TUMS-RTC算法相较于对比算法有更高的资源利用率,虚拟机数量减少率和能耗节省率,并且可以很好地处理云计算中规模大且并行度高的工作流。     

云数据中心 I/O 资源池化

近年来,云计算的发展为数据中心带来了新的应用场景和需求.其中,虚拟化作为云服务的重要使能技术,对数据中心服务器I/O系统的性能、扩展性和设备种类多样性提出了更高的要求,沿用传统设备与服务器紧耦合的I/O架构将会导致资源冗余,数据中心服务器密度降低,布线复杂度增加等诸多问题.因此,文章围绕I/O资源池化架构的实现机制和方法展开研究,目标是解除设备与服务器之间的绑定关系,实现接入服务器对I/O资源的按需弹性化使用,从根本上解决云计算数据中心的I/O系统问题.同时,还提出了一种基于单根I/O虚拟化协议实现多根I/O资源池化的架构,该架构通过硬件的外设部件高速互连接口多根域间地址和标识符映射机制,实现了多个物理服务器对同一I/O设备的共享复用;通过虚拟I/O设备热插拔技术和多根共享管理机制,实现了虚拟I/O资源在服务器间的实时动态分配;采用现场可编程门阵列(Field-Programmable Gate Array)构建了该架构的原型系统.结果表明,该架构能够为各个共享服务器提供良好的I/O操作性能.     

A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Cloud-based data centers consume a significant amount of energy which is a costly procedure. Virtualization technology, which can be regarded as the first step in the cloud by offering benefits like the virtual machine and live migration, is trying to overcome this problem. Virtual machines host workload, and because of the variability of workload, virtual machines consolidation is an effective technique to minimize the total number of active servers and unnecessary migrations and consequently improves energy consumption. Effective virtual machine placement and migration techniques act as a key issue to optimize the consolidation process. In this paper, we present a novel virtual machine consolidation technique to achieve energy–QoS–temperature balance in the cloud data center. We simulated our proposed technique in CloudSim simulation. Results of evaluation certify that physical machine temperature, SLA, and migration technique together control the energy consumption and QoS in a cloud data center.     

Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment

In most cloud computing platforms, the virtual machine quotas are seldom changed once initialized, although the current allocated resources are not efficiently utilized. The average utilization of cloud servers in most datacenters can be improved through virtual machine placement optimization. How to dynamically forecast the resource usage becomes a key problem. This paper proposes a scheduling algorithm called virtual machine dynamic forecast scheduling (VM-DFS) to deploy virtual machines in a cloud computing environment. In this algorithm, through analysis of historical memory consumption, the most suitable physical machine can be selected to place a virtual machine according to future consumption forecast. This paper formalizes the virtual machine placement problem as a bin-packing problem, which can be solved by the first-fit decreasing scheme. Through this method, for specific virtual machine requirements of applications, we can minimize the number of physical machines. The VM-DFS algorithm is verified through the CloudSim simulator. Our experiments are carried out on different numbers of virtual machine requests. Through analysis of the experimental results, we find that VM-DFS can save 17.08 % physical machines on the average, which outperforms most of the state-of-the-art systems.     

Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

In this paper, we propose a server architecture recommendation and automatic performance verification technology, which recommends and verifies appropriate server architecture on Infrastructure as a Service (IaaS) cloud with bare metal servers, container-based virtual servers and virtual machines. Recently, cloud services are spread, and providers provide not only virtual machines but also bare metal servers and container-based virtual servers. However, users need to design appropriate server architecture for their requirements based on three types of server performances, and users need much technical knowledge to optimize their system performance. Therefore, we study a technology that satisfies users’ performance requirements on these three types of IaaS cloud. Firstly, we measure performance and start-up time of a bare metal server, Docker containers, KVM (Kernel-based Virtual Machine) virtual machines on OpenStack with changing number of virtual servers. Secondly, we propose a server architecture recommendation technology based on the measured quantitative data. A server architecture recommendation technology receives an abstract template of OpenStack Heat and function/performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology that executes necessary performance tests automatically on provisioned user environments according to the template. We implement proposed technologies, confirm performance and show the effectiveness.     

Server Selection,Configuration and Reconfiguration Technology for IaaS Cloud with Multiple Server Types

We propose a server selection, configuration, reconfiguration and automatic performance verification technology to meet user functional and performance requirements on various types of cloud compute servers. Various servers mean there are not only virtual machines on normal CPU servers but also container or baremetal servers on strong graphic processing unit (GPU) servers or field programmable gate arrays (FPGAs) with a configuration that accelerates specified computation. Early cloud systems are composed of many PC-like servers, and virtual machines on these severs use distributed processing technology to achieve high computational performance. However, recent cloud systems change to make the best use of advances in hardware power. It is well known that baremetal and container performances are better than virtual machines performances. And dedicated processing servers, such as strong GPU servers for graphics processing, and FPGA servers for specified computation, have increased. Our objective for this study was to enable cloud providers to provision compute resources on appropriate hardware based on user requirements, so that users can benefit from high performance of their applications easily. Our proposed technology select appropriate servers for user compute resources from various types of hardware, such as GPUs and FPGAs, or set appropriate configurations or reconfigurations of FPGAs to use hardware power. Furthermore, our technology automatically verifies the performances of provisioned systems. We measured provisioning and automatic performance verification times to show the effectiveness of our technology.     

An adaptive resource management scheme in cloud computing

There are various significant issues in resource allocation, such as maximum computing performance and green computing, which have attracted researchers’ attention recently. Therefore, how to accomplish tasks with the lowest cost has become an important issue, especially considering the rate at which the resources on the Earth are being used. The goal of this research is to design a sub-optimal resource allocation system in a cloud computing environment. A prediction mechanism is realized by using support vector regressions (SVRs) to estimate the number of resource utilization according to the SLA of each process, and the resources are redistributed based on the current status of all virtual machines installed in physical machines. Notably, a resource dispatch mechanism using genetic algorithms (GAs) is proposed in this study to determine the reallocation of resources. The experimental results show that the proposed scheme achieves an effective configuration via reaching an agreement between the utilization of resources within physical machines monitored by a physical machine monitor and service level agreements (SLA) between virtual machines operators and a cloud services provider. In addition, our proposed mechanism can fully utilize hardware resources and maintain desirable performance in the cloud environment.     

异构云平台中能源有效的虚拟机部署研究

能源消耗已经成为数据中心操作成本的重要组成部分,虚拟化技术是降低数据中心能源消耗的有效方法之一.为了降低数据中心过高的能源消耗,利用虚拟化技术,结合数据中心中物理机的异构性和虚拟机所需资源的多维性,提出了一个衡量不同类型物理机性能的模型和一个衡量多维资源利用率的模型,在此基础上提出了一个异构云平台下能源有效的虚拟机部署算法.仿真实验表明,与MBFD算法及BFD算法相比,该算法不仅可以有效地降低系统的能源消耗,而且还提高了资源利用率,减少了资源的浪费.     

Stochastic modeling and analysis of hybrid mobility in reconfigurable distributed virtual machines

Virtualization provides a vehicle to manage the available resources and enhance their utilization in network computing. System dynamics requires virtual machines be distributed and reconfigurable. To construct reconfigurable distributed virtual machines, service migration moves the runtime services among physical servers when necessary. By incorporating the mobile agent technology, distributed virtual machines can improve their resource utilization and service availability significantly. This paper focuses on finding the optimal migration policies for service and agent migrations for high throughput in reconfigurable distributed virtual machines. We analyze three issues of this decision problem: migration candidate determination, migration timing and destination server selection. The service migration timing and destination server selection are formulated by two optimization models. We derive the optimal migration policy for distributed and heterogenous systems based on stochastic optimization theories. Renewal processes are applied to model the dynamics of migration. We solve the agent migration problem by dynamic programming and extend the optimal service migration decision by considering the interplay of the hybrid mobility. We verify the accuracy of our migration decision policy in simulations.