Adaptive parallel application resource remapping through the live migration of virtual machines

In this paper we present ARRIVE-F, a novel open source framework which addresses the issue of heterogeneity in virtualized compute farms, such as those hosted by a cloud infrastructure provider. Unlike the previous attempts, our framework is not based on linear frequency models and does not require source code modifications or off-line profiling. The heterogeneous compute farm is first divided into a number of homogeneous sub-clusters. The framework then carries out a lightweight ‘online’ profiling of the CPU, communication and memory subsystems of all the active jobs in the compute farm. From this, it constructs a performance model to predict the execution times of each job on all the distinct sub-clusters in the compute farm. Based upon the predicted execution times, the framework is able to relocate the compute jobs to the currently best-suited hardware platforms such that the overall throughput of the compute farm is increased. We utilize the live migration feature of virtual machine monitors to migrate the job from one sub-cluster to another.The prediction accuracy of our performance estimation model is over 80%. The implementation of ARRIVE-F is lightweight, with an overhead of 3%. Experiments on a synthetic workload of scientific benchmarks show that we are able to improve the throughput of a moderately heterogeneous compute farm by up to 25%, with a time saving of up to 33%.     

A method for managing green power of a virtual machine cluster in cloud

A green power management scheme is proposed to determine how many physical machines should be run or turned off based on the gross occupied resource weight ratio of the virtual machine cluster. The gross occupied resource weight ratio is defined as the ratio of the sum of resource weights of all virtual machines over the sum of available resource weights of all running physical machines. When the gross occupied resource weight ratio is greater than the maximum tolerant occupied resource weight ratio, preset to ensure quality of service, a standby physical machine in the non-running physical machines is selected and wakened up to join as one of the running physical machines. On the other hand, when the gross occupied resource weight ratio is less than the minimum critical occupied resource weight ratio, preset to trigger energy saving algorithms, one of the running physical machines, selected as a migration physical machine with the virtual machines therein removed after live migration, is moved from other running physical machines, and then turned off. As a result, a resource allocation process is realized to distribute loads of the running physical machines such that the total number of the running physical machines can be flexibly dispatched to achieve the objective of green power management.     

MECOM: Live migration of virtual machines by adaptively compressing memory pages

Live migration of virtual machines has been a powerful tool to facilitate system maintenance, load balancing, fault tolerance, and power-saving, especially in clusters or data centers. Although pre-copy is extensively used to migrate memory data of virtual machines, it cannot provide quick migration with low network overhead but leads to large performance degradation of virtual machine services due to the great amount of transferred data during migration. To solve the problem, this paper presents the design and implementation of a novel memory-compression-based VM migration approach (MECOM for short) that uses memory compression to provide fast, stable virtual machine migration, while guaranteeing the virtual machine services to be slightly affected. Based on memory page characteristics, we design an adaptive zero-aware compression algorithm for balancing the performance and the cost of virtual machine migration. Using the proposed scheme pages are rapidly compressed in batches on the source and exactly recovered on the target. Experimental results demonstrate that compared with Xen, our system can significantly reduce downtime, total migration time, and total transferred data by 27.1%, 32%, and 68.8% respectively.     

一种迁移开销感知的虚拟机动态整合算法

现有的以降低能耗为目标的虚拟机动态整合算法通常忽略了虚拟机迁移所带来的消极影响,导致虚拟机的动态整合虽然减少了数据中心的能耗,但不合理的虚拟机迁移次数较多,极有可能增加了SLA（Service Level Agreements）的违例率。针对上述问题,提出了一种迁移开销感知的虚拟机动态整合算法MigCAP（Migration Cost Aware Policy）,定义了迁移收益参数EMP,MigCAP算法通过EMP值的大小来决定是否需要进行虚拟机的迁移,避免了不合理的虚拟机迁移的发生。实验结果表明,MigCAP算法与现有的其他虚拟机动态整合算法相比,能够在有效减少能耗和降低SLA违例率的基础上,显著减少虚拟机迁移次数。     

基于Linux的RHEL集群虚拟化系统的研究

通过对现有服务器集群系统的虚拟化系统和集群系统进行分析,采用虚拟化和Linux技术将虚拟化、负载均衡集群和高可用集群整合在一起,实现了三层构架的WWW服务器集群,从而以较低的价格达到了昂贵的商业硬件存储解决方案所实现的效果,从而有效克服现有集群技术的缺点。     

Seamless live migration of virtual machines over the MAN/WAN

The “VM Turntable” demonstrator at iGRID 2005 pioneered the integration of Virtual Machines (VMs) with deterministic “lightpath” network services across a MAN/WAN. The results provide for a new stage of virtualization—one for which computation is no longer localized within a data center but rather can be migrated across geographical distances, with negligible downtime, transparently to running applications and external clients. A noteworthy data point indicates that a live VM was migrated between Amsterdam, NL and San Diego, USA with just 1–2 s of application downtime. When compared to intra-LAN local migrations, downtime is only about 5–10 times greater despite 1000 times higher round-trip times.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

面向云计算的多虚拟机管理模型的设计

提出基于P2P结构的多虚拟机管理模型,并实现其原型系统。采用P2P结构组织宿主机节点,利用组播实现资源发现;提出虚拟机动态迁移算法,自主触发节点间虚拟机动态迁移;利用选举的根节点映射云计算用户请求至宿主机,实现节点上虚拟机的按需创建、删除及停止等操作。实验表明:该模型具有收敛时间短、带宽占用率低及高可用性的特点,能够实现云计算资源的动态负载均衡。     

基于虚拟机的高职学生Linux实践平台的构建

介绍了虚拟机技术、虚拟机平台的搭建、VmwareWorkstation虚拟网络设备等,重点讨论了通过VMware构建虚拟实验平台以解决Linux实践教学中真实实验环境欠缺的问题,以做到教学演示与学生实践的同步,为Linux实践教学提供一种有效的辅助工具。     

Towards an architecture for virtual enterprises

An approach to the design of an architecture for industrial virtual enterprises (VE), with special emphasis on the identification of main functional requirements, is presented. First, a discussion of the various types of virtual enterprises is provided. Some classification scenarios and discussions of the modelling and reengineering tools and methodologies are described. Due to the importance of the information flows and management in the VE, one section is merely devoted to the analysis of the appropriateness of a federated information management approach. This work is based on and represents the ongoing activities in two European Union funded projects; the Esprit PRODNET II and the INCO-DC SCM+ projects.     

基于PABFD-SA算法的虚拟机动态迁移研究

虚拟机动态迁移整合技术是大规模异构云数据中心降低能耗的有效方法。采用指数平滑预测法进行负载检测,然后以最小迁移时间算法（MMT）为原则筛选出待重分配的虚拟机,并就重分配过程中的能耗优化问题设计了一种感知能耗的最佳适配递减和模拟退火组合算法PABFD-SA（Power Aware Best Fit Decreasing-Simulated Annealing）。该算法将BFD算法获取的物理主机序列作为SA算法的初始解,并在搜索过程中加入了保留和更新历史最优解的功能。仿真结果表明,该算法在减少异构云计算系统的总能耗,降低SLA违约方面有一定改善。     

校园网虚拟化数据中心的构建及其应用

在数字化程度越来越高、信息化越来越深入的校园网环境中,传统的以物理服务器堆砌构建的数据中心,存在着管理复杂、资源利用率低、备份困难等问题,难以满足学校信息化不断发展的需要。数据中心向虚拟化、动态化转变,通过构建虚拟化统一管理平台,整合和动态调配物理服务器资源,大大提高了服务器的使用效率和管理执行力,数据和业务安全保障也变得简单易行。     

Proactive workload management in dynamic virtualized environments

Recently, with the improvement of Cloud systems technologies and the essential advantages they can provide such as availability, scalability, and costs saving; massive domains in the IT industry are directing their business to the Cloud. To fit the computing demands of this trend along with nowadays fluky applications (e.g. social networks, media contents), Cloud systems require rapid resource changes. As a result, the workload management in a virtualized environment becomes a complex task. In this paper we propose a new proactive workload management model for virtualized resources to inspect the workload behavior of the running Virtual Machines, and to assent an appropriate scheduling and resource consolidation schema in order to improve the system efficiency, utilization, and throughput. We have carried out our model by modifying Xen Cloud Platform, then we tested the model performance through different representative benchmarks. The results show that the Proactive model can decrease the average response time remarkably.     

面向高校教学云服务的虚拟机动态调度方法

随着教学信息化的不断深化,高校云平台越来越普及,但是实际应用中资源利用率仍然较低,核心问题在于当前的虚拟机调度机制未考虑高校教学应用的特征,从而导致负载不均和资源浪费。为了解决这一问题,从高校教学应用需求出发,提出了一种虚拟机动态调度算法(CRS),定义了课程虚拟机模型和物理机负载模型,并实现了基于OpenStack开源云平台的可对虚拟机进行动态调度的校园云平台。实验表明,提出的虚拟机动态调度方法达到了降低能耗及实现负载均衡的目标。     

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

Cloud computing is an essential part of today’s computing world. Continuously increasing amount of computation with varying resource requirements is placed in large data centers. The variation among computing tasks, both in their resource requirements and time of processing, makes it possible to optimize the usage of physical hardware by applying cloud technologies. In this work, we develop a prototype system for load-based management of virtual machines in an OpenStack computing cluster. Our prototype is based on an idea of ‘packing’ idle virtual machines into special park servers optimized for this purpose. We evaluate the method by running real high-energy physics analysis software in an OpenStack test cluster and by simulating the same principle using the Cloudsim simulator software. The results show a clear improvement, 9–48 % , in the total energy efficiency when using our method together with resource overbooking and heterogeneous hardware.     

OpenFlow网络中虚拟网络分片的动态迁移

随着数据中心技术的发展和需要,以OpenFlow架构为代表的网络虚拟化相关技术得到了极大发展。本文对于虚拟网络分片中的迁移技术进行探讨,提出一种解决方案,通过对OpenFlow网络的虚拟化层FlowVisor的修改,完成虚拟网络分片的动态迁移,并保证在此过程中满足以下条件:1、必须是热迁移,对虚拟网络分片用户透明,2、具有安全性,保证在迁移过程中不会丢包。     

虚拟集群上面向功耗的形式化的VM调度策略

针对虚拟化集群上日益严重的功耗问题,在定义集群、物理机、操作点、能耗、任务集、虚拟机等概念的基础上,提出了形式化的虚拟机(Virtual Machine,VM)调度策略,并有针对性地利用动态电压和频率调整(Dynamic Voltage and Frequency Scaling,DVFS)技术对普通算法进行了扩展改进,提出一个功耗敏感的VM调度算法。首先,利用FFD(First-Fit Decreasing)算法解决虚拟机的初始布局问题,然后尽可能地将虚拟机部署在低电压的物理机上,此外加入性能感知策略,尽量将物理机的电压调节至刚好满足虚拟机性能需求,以避免"奢侈"能耗的浪费,同时增加了虚拟机调度失败时灵活调整电压的规则,以减少错误先验知识对虚拟机后续部署的误导。在理想模型中的仿真实验表明:与现有部署算法相比,本算法具有更好的节能效应,以及相对不大的性能损失。     

面向动态负载的集群容器部署方法

针对集群负载动态变化引发容器频繁迁移的问题,提出了一种基于资源预留的容器部署方法.首先,设计了基于马尔可夫链模型的单容器资源需求动态变化描述机制,用于刻画单容器的资源需求情况;其次,基于单容器马尔可夫链模型分析了多容器资源动态变化情况,以刻画容器资源需求态势;随后,基于多容器马尔可夫链提出了面向动态负载的容器部署与资源...