VM3: Measuring,modeling and managing VM shared resources

With cloud and utility computing models gaining significant momentum, data centers are increasingly employing virtualization and consolidation as a means to support a large number of disparate applications running simultaneously on a chip-multiprocessor (CMP) server. In such environments, contention for shared platform resources (CPU cores, shared cache space, shared memory bandwidth, etc.) can have a significant effect on each virtual machine’s performance. In this paper, we investigate the shared resource contention problem for virtual machines by: (a) measuring the effects of shared platform resources on virtual machine performance, (b) proposing a model for estimating shared resource contention effects, and (c) proposing a transition from a virtual machine (VM) to a virtual platform architecture (VPA) that enables transparent shared resource management through architectural mechanisms for monitoring and enforcement. Our measurement and modeling experiments are based on a consolidation benchmark (vConsolidate) running on a state-of-the-art CMP server. Our virtual platform architecture experiments are based on detailed simulations of consolidation scenarios. Through detailed measurements and simulations, we show that shared resource contention affects virtual machine performance significantly and emphasize that virtual platform architectures is a must for future virtualized datacenters.     

Dynamic resource demand prediction and allocation in multi‐tenant service clouds

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

云计算架构下的深层次动态资源调配

云计算是当前学术界和工业界都十分关注的热点,被广泛应用于针对海量数据和用户的大规模计算。云计算的特点要求计算机系统能够提供可伸缩的计算能力,而虚拟化技术正是其中的关键层次,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。通过虚拟化技术,可以实现一个多层次的资源调度机制,以保证高资源利用率和系统性能:首先面向虚拟机的应用特征建立资源预测模型,然后依据预测结果建立资源分配策略,最终通过虚拟机间的资源动态优化技术,实现在同一物理主机或不同物理主机上虚拟机间动态的资源优化使用。这里,不仅要以物理机的宏观资源利用率作为管理依据,更需要关注虚拟机上应用程序在运行过程中的资源需求变化特征,从而为云计算提供一整套的虚拟化资源优化技术及使用方案,从静态部署、动态预测、单机资源动态调配、多机资源动态均衡调度、在线迁移等多个层次为云计算提供全面、有机的支撑。     

Accelerator Virtualization Framework Based on Inter-VM Exitless Communication

The increasing deployment of artificial intelligence has placed unprecedent requirements on the computing power of cloud computing. Cloud service providers have integrated accelerators with massive parallel computing units in the data center. These accelerators need to be combined with existing virtualization platforms to partition the computing resources. The current mainstream accelerator virtualization solution is through the PCI passthrough approach, which however does not support fine-grained resource provisioning. Some manufacturers also start to provide time-sliced multiplexing schemes and use drivers to cooperate with specific hardware to divide resources and time slices to different virtual machines, which unfortunately suffer from poor portability and flexibility. One alternative but promising approach is based on API forwarding, which forwards the virtual machine''s request to the back-end driver for processing through a separate driver model. Yet, the communication due to API forwarding can easily become the performance bottleneck. This paper proposes Wormhole, an accelerator virtualization framework based on the C/S architecture that supports rapid delegated execution across virtual machines. It aims to provide upper-level users with an efficient and transparent way to accelerate the virtualization of accelerators with API forwarding while ensuring strong isolation between multiple users. By leveraging hardware virtualization feature, the framework minimizes performance degradation through exitless inter-VM control flow switch. Experimental results show that Wormhole''s prototype system can achieve up to 5 times performance improvement over the traditional open-source virtualization solution such as GVirtuS in the training test of the classic model.     

基于微内核的虚拟机I/O安全机制

NOVA等微内核虚拟化架构解决了宏内核平台可信计算基体积和攻击面过大的问题, 但其仍缺乏虚拟机分等级保护和I/O资源访问控制等安全机制. 本文提出了安全域的概念, 并将虚拟机划分至不同的安全域, 进而建立可定制的I/O资源访问控制机制. 通过将访问控制模块添加至I/O资源访问的关键代码路径, 实现了不同安全域的I/O资源访问控制. 实验表明, 该机制提高了数据的隔离性与安全性, 仅对计算密集型、I/O密集型任务造成了较小的性能损耗.     

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter‐networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter‐networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy‐efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

企业虚拟化服务器架设的研究

服务器虚拟化架设将服务器物理资源抽象成逻辑资源,让一台服务器变成几台相互隔离的虚拟服务器,再在虚拟服务器部署各应用以共享一台服务器的资源,这样我们将不再受限于物理上的界限,而是让CPU、内存、磁盘等硬件变成可以动态管理的“资源池”,从而提高资源的利用率,简化系统管理,实现服务器整合,以达到资源充分利用的最终目的。     

基于资源需求分布特征的异构云环境虚拟机放置算法

针对异构云环境中的虚拟机放置（VMP）问题,提出一种基于虚拟机资源需求分布特征的放置算法（RDDFPA）。首先,建立基于CPU资源和内存资源比例系数的虚拟机需求和物理机配置描述方法,并根据该比例系数对所有虚拟机进行排序;其次,通过分析虚拟机需求与物理机配置各自在CPU资源和内存资源比例方面的关系,确定比例分界点,完成虚拟机集合的划分,每个虚拟机子集合的规模反映出对相匹配的不同配置物理机的需求比例;最后,利用启发式算法如首次适应（First Fit）算法完成虚拟机子集合在相匹配配置的物理机子集合上的放置。理论分析和仿真实验结果表明,与采用任意单一配置的物理机总数量相比,所提算法所需物理机的总台数减少了2%~17%。RDDFPA能够根据虚拟机资源需求分布的不同,确定各类配置物理机的数量,高效完成虚拟机的放置,在提高资源利用率的同时,降低了系统能耗。     

Scalability Comparison of Four Host Virtualization Tools

Virtualization tools are becoming popular in the context of Grid Computing because they allow running multiple operating systems on a single host and provide a confined execution environment. In several Grid projects, virtualization tools are envisioned to run many virtual machines per host. This immediately raises the issue of virtualization scalability. In this paper, we compare the scalability merits of Four virtualization tools. First, from a simple experiment, we motivate the need for simple microbenchmarks. Second, we present a set of metrics and related methodologies. We propose four microbenchmarks to measure the different scalability parameters for the different machine resources (CPU, memory disk and network) on three scalability metrics (overhead, linearity and isolation). Third, we compare four virtual machine technologies (Vserver, Xen, UML and VMware). The results of this study demonstrate that all the compared tools present different behaviors with respect to scalability, in terms of overhead, resource occupation and isolation. Thus this work will help user to select tools according to their application characteristics.     

PCVM.ARIMA: predictive consolidation of virtual machines applying ARIMA method

Cloud computing adopts virtualization technology, including migration and consolidation of virtual machines, to overcome resource utilization problems and minimize energy consumption. Most of the approaches have focused on minimizing the number of physical machines and rarely have devoted attention to minimizing the number of migrations. They also decide based on the current resources utilization without considering the demand for resources in the future. Some approaches minimize the number of active physical machines and Service Level Agreement (SLA) violations with the number of unnecessary migrations. They consider the current resource utilization of physical machines and neglect from demands for future resource requirements. As a result, as time passes, the number of unnecessary migrations, and subsequently, the rate of SLA violations in data centers increases. Alternatively, several approaches only focus on a hardware level and reduce the physical machine’s dynamic power consumption. The lack of control over the overload of physical machines increases the amount of violation. In this paper, a framework called PCVM.ARIMA is presented that focuses on the dynamic consolidation of virtual machines over the minimum number of physical machines, minimize the number of unnecessary migrations, detect the physical machine overloading, and SLA based on the ARIMA prediction model. Moreover, the Dynamic Voltage and Frequency Scaling (DVFS) technique is used to apply the optimal frequency to heterogeneous physical machines. The experimental results show that the presented framework significantly reduces energy consumption while it improves the QoS factors in comparison to some baseline methods.

     

An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user’s QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.

     

计算系统虚拟化平台的研究及实现

虚拟化技术是高性能计算系统规模化的关键技术。高能所计算资源虚拟实验床采用 OpenStack 云平台搭建环境。本文讨论了实现虚拟计算资源与计算系统相互融合的三个关键因素：网络架构设计、环境匹配和系统总体规划。本文首先讨论了虚拟网络架构。虚拟化平台通过部署 neutron 组件、OVS以及 802.1Q 协议来实现虚拟网络和物理网络的二层直连,通过配置物理交换机实现三层转发,避免了数据经过 OpenStack 网络节点转发的瓶颈。其次,虚拟计算资源要融入计算系统,需要与计算系统的各个组件进行信息的动态同步,以满足域名分配、自动化配置以及监视等系统的需要。文章介绍了自主开发的 NETDB 组件,该组件负责实现包括虚拟机与域名系统 (DNS)、自动化安装和管理系统 (puppet) 以及监视系统的信息动态同步等功能;最后,在系统总体规划中,文章讨论了包括统一认证、共享存储、自动化部署、规模扩展和镜像等内容。     

Precise contention-aware performance prediction on virtualized multicore system

Multicore systems are widely deployed in both the embedded and the high end computing infrastructures. However, traditional virtualization systems can not effectively isolate shared micro architectural resources among virtual machines (VMs) running on multicore systems. CPU and memory intensive VMs contending for these resources will lead to serious performance interference, which makes virtualization systems less efficient and VM performance less stable. In this paper, we propose a contention-aware performance prediction model on the virtualized multicore systems to quantify the performance degradation of VMs. First, we identify the performance interference factors and design synthetic micro-benchmarks to obtain VM’s contention sensitivity and intensity features that are correlated with VM performance degradation. Second, based on the contention features, we build VM performance prediction model using machine learning techniques to quantify the precise levels of performance degradation. The proposed model can be used to optimize VM performance on multicore systems. Our experimental results show that the performance prediction model achieves high accuracy and the mean absolute error is 2.83%.     

Improving performance by network-aware virtual machine clustering and consolidation

Modern data center consists of thousands of servers, racks and switches. Complicated structure means it requires well-designed algorithms to utilize resources of data centers efficiently. Current virtual machine scheduling algorithms mainly focus on the initial allocation of virtual machines based on the CPU, memory and network bandwidth requirements. However, when tasks finished or lease expired, related virtual machines would be deleted from the system which would generate resource fragments. Such fragments lead to unbalanced resource utilization and decline of communication performance. This paper investigates the network influence on typical applications in data centers and proposed a self-adaptive network-aware virtual machine clustering and consolidation algorithm to maintain an optimal system-wide status. Our consolidation algorithm periodically checks whether consolidation is necessary and then clusters and consolidates virtual machines to lower communication cost with an online heuristic. We used two benchmarks in a real environment to examine network influence on different tasks. To evaluate the advantages of the proposed algorithm, we also built a cloud computing testbed. Real workload trace-driven simulations and testbed-based experiments showed that, our algorithm greatly shortened the average finish time of map-reduce tasks and reduced time delay of web applications. Simulation results showed that our algorithm considerably reduced the amount of high-delay jobs, lowered the average traffic passed through aggregate switches and improved the communication ability among virtual machines.     

基于LXC的Android系统虚拟化技术

虚拟化技术的研究正逐渐从高性能服务器端转向移动智能设备领域. 现有的虚拟化方案多是采用多内核方案,系统负载高,效率低. 针对车载系统等平台多屏显示以及资源受限等问题,本文提出一种基于容器技术的Android轻量级虚拟化方案. 该方案通过利用Namespace资源隔离机制和Cgroup资源分配机制,使得ARM平台在资源使用较少的同时,能够同时启动多个Android虚拟机,并且各虚拟机上的屏幕显示相互独立. 测试结果表明,该方案的内存占用率较双系统方案降低了7%,而平均CPU使用率较原生Android系统仅增加了1%.     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

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.     

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Parallel Workload Archive（并行工作负载存档）的真实工作负载数据进行了仿真实验,结果表明该方法可保证为云供应商带来更高收入,提高资源利用率。