Failure-aware resource management for high-availability computing clusters with distributed virtual machines

In large-scale networked computing systems, component failures become norms instead of exceptions. Failure-aware resource management is crucial for enhancing system availability and achieving high performance. In this paper, we study how to efficiently utilize system resources for high-availability computing with the support of virtual machine (VM) technology. We design a reconfigurable distributed virtual machine (RDVM) infrastructure for networked computing systems. We propose failure-aware node selection strategies for the construction and reconfiguration of RDVMs. We leverage the proactive failure management techniques in calculating nodes’ reliability states. We consider both the performance and reliability status of compute nodes in making selection decisions. We define a capacity–reliability metric to combine the effects of both factors in node selection, and propose Best-fit algorithms with optimistic and pessimistic selection strategies to find the best qualified nodes on which to instantiate VMs to run user jobs. We have conducted experiments using failure traces from production systems and the NAS Parallel Benchmark programs on a real-world cluster system. The results show the enhancement of system productivity by using the proposed strategies with practically achievable accuracy of failure prediction. With the Best-fit strategies, the job completion rate is increased by 17.6% compared with that achieved in the current LANL HPC cluster. The task completion rate reaches 91.7% with 83.6% utilization of relatively unreliable nodes.     

Developing resource consolidation frameworks for moldable virtual machines in clouds

This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as “rigid” during consolidation, i.e., VMs’ resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs’ resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as “moldable” during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm.     

Efficient resource management for virtual desktop cloud computing

In virtual desktop cloud computing, user applications are executed in virtual desktops on remote servers. This offers great advantages in terms of usability and resource utilization; however, handling a large amount of clients in the most efficient manner poses important challenges. Especially deciding how many clients to handle on one server, and where to execute the user applications at each time is important. Assigning too many users to one server leads to customer dissatisfaction, while assigning too little leads to higher investments costs. We study different aspects to optimize the resource usage and customer satisfaction. The results of the paper indicate that the resource utilization can increase with 29% by applying the proposed optimizations. Up to 36.6% energy can be saved when the size of the online server pool is adapted to the system load by putting redundant hosts into sleep mode.     

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。     

Resource management framework for collaborative computing systems over multiple virtual machines

A resource management framework for collaborative computing systems over multiple virtual machines (CCSMVM) is presented to increase the performance of computing systems by improving the resource utilization, which has constructed a scalable computing environment for resource on-demand utilization. We design a resource management framework based on the advantages of some components in grid computing platform, virtualized platform and cloud computing platform to reduce computing systems overheads and maintain workloads balancing with the supporting of virtual appliance, Xen API, applications virtualization and so on. The content of collaborate computing, the basis of virtualized resource management and some key technologies including resource planning, resource allocation, resource adjustment and resource release and collaborative computing scheduling are designed in detail. A prototype is designed, and some experiments have verified the correctness and feasibility of our prototype. System evaluations show that the time in resource allocation and resource release is proportional to the quantity of virtual machines, but not the time in the virtual machines migrations. CCSMVM has higher CPU utilization and better performance than other systems, such as Eucalyptus 2.0, Globus4.0, et al. It is concluded that CCSMVM can accelerate the execution of systems by improving average CPU utilization from the results of comparative analysis with other systems, so it is better than others. Our study on resource management framework has some significance to the optimization of the performance in virtual computing systems.     

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%.     

Hierarchical control policy for dynamic resource management in grid virtual organization

This paper proposes a hierarchical control system in grid virtual organization. The hierarchical system can be decomposed into multiple application groups, which can be further decomposed into multiple applications. At the top of the hierarchy, the global controller controls the gross allocation of resources to the groups. At the next level down, the group controller coordinates the local deployments of all applications that consume the local allocation of resources. At the lowest level, the local controllers adjust the local resource usages to optimize the utility of single application. The hierarchical control system considers all applications and coordinates all layers of grid architecture upon any changes. According to different time granularity, we adopt a different control scheme. The global control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity, while local control adapts a single application to small changes at fine granularity. This paper adopts utility-driven cross layer optimization for grid applications to find a system wide optimization and solves the cross-layer optimization by using pricing based decomposition. A set of hierarchical utility functions is used to measure the performance of the grid system that follows the system, group and application hierarchy. This paper uses total utility to measure the overall quality of grid system. The experiments are conducted to test the performance of the hierarchical control algorithms.

VMStore: Distributed storage system for multiple virtual machines

Desktop virtualization is a very hot concept in both industry and academic communities. Since virtualized desktop system is based on multiple virtual machines (VM), it is necessary to design a distributed storage system to manage the VM images. In this paper, we design a distributed storage system, VMStore, by taking into account three important characteristics: high performance VM snapshot, booting optimization from multiple images and redundancy removal of images data. We adopt a direct index structure of...     

云数据中心虚拟资源管理研究综述

云计算数据中心是数据中心架构和发展的方向,云数据中心的虚拟资源管理问题是当前的研究热点。总结了数据中心的发展历程,综述了云数据中心的虚拟化技术、虚拟资源提供与部署、资源调度模型与算法以及基于能量优化和负载均衡的虚拟机迁移技术的研究现状。最后展望了云数据中心虚拟资源管理领域有待进一步研究的方向。     

An interference-aware virtual clustering paradigm for resource management in cognitive femtocell networks

Femtocells represent a promising alternative solution for high quality wireless access in indoor scenarios where conventional cellular system coverage can be poor. They are randomly deployed by the end user, so only post-deployment network planning is possible. Furthermore, this uncoordinated deployment creates severe interference to co-located femtocells, especially in dense deployments. This paper presents a new architecture using a generalised virtual cluster femtocell (GVCF) paradigm, which groups together FAP into logical clusters. It guarantees severely interfering and overlapping femtocells are assigned to different clusters. Since each cluster operates on different band of frequencies, the corresponding virtual cluster controller only has to manage its own FAPs, so the overall system complexity is low. The performance of the GVCF algorithm is analysed from both a resource availability and cluster number perspective. Simulation results conclusively corroborate the superior performance of the GVCF model in interference mitigation, particularly in high density FAP scenarios.     

DMM:A dynamic memory mapping model for virtual machines

Memory virtualization is an important part in the design of virtual machine monitors(VMM).In this paper,we proposed dynamic memory mapping(DMM) model,a mechanism that allows the VMM to change the mapping between a virtual machine's physical memory and the underlying hardware resource while the virtual machine is running.By utilizing DMM,the VMM can implement many novel memory management policies,such as Demand Paging,Swapping,Ballooning,Memory Sharing and Copy-On-Write,while preserving compatibility with va...     

面向虚拟路由器平台的资源管理平面的设计与实现

通过研究与分析虚拟路由器平台在管理与维护等方面存在的问题,提出了三层的虚拟路由器体系框架：控制平面、转发平面和资源管理平面。控制平面和转发平面是虚拟路由器的两个基本功能平面,分别承载逻辑控制平面和逻辑转发平面。为了动态地管理虚拟路由器平台,引入了资源管理平面。它是管理物理资源和维护路由器实例的重要功能平面。通过阐述资源管理平面的框架以及实现机制,实现了支持路由器实例的静态创建和动态调整的原型系统。实验结果表明：资源管理平面的最大处理能力为3205条命令/秒,在1 min内完成路由器实例的创建任务,能够满足虚拟路由器平台的管理需求。     

Modeling a resource contention in the management of virtual organizations

A virtual organization provides a cost-efficient method allowing different autonomous entities, such as organizations, departments and individuals, to extend service offerings in a virtual marketplace. To support cost-efficient service provisioning, a suitable procedure must be applied to determine the amount of resources necessary for the operation of virtual organizations.We propose a new mathematical model for a quantitative performance evaluation of resource management in virtual organizations. We present an efficient algorithm to determine the steady state probabilities and the performance measures of the system. A comparison with a detailed simulation model and other numerical approaches shows that the proposed algorithm is fast and accurate. This algorithm can therefore be used for resource dimensioning to support the cost-efficient operation of virtual organizations.     

Optimization of virtual resource management for cloud applications to cope with traffic burst

Being the latest computing paradigm, cloud computing has proliferated as many IT giants started to deliver resources as services. Thus application providers are free from the burden of the low-level implementation and system administration. Meanwhile, the fact that we are in an era of information explosion brings certain challenges. Some websites may encounter a sharp rising workload due to some unexpected social concerns, which make these websites unavailable or even fail to provide services in time. Currently, a post-action method based on human experience and system alarm is widely used to handle this scenario in industry, which has shortcomings like reaction delay. In our paper, we want to solve this problem by deploying such websites on cloud, and use features of the cloud to tackle it. We present a framework of dynamic virtual resource management in clouds, to cope with traffic burst that applications might encounter. The framework implements a whole work-flow from prediction of the sharp rising workload to a customized resource management module which guarantees the high availability of web applications and cost-effectiveness of the cloud service providers. Our experiments show the accuracy of our workload forecasting method by comparing it with other methods. The 1998 World Cup workload dataset used in our experiment reveals the applicability of our model in the specific scenarios of traffic burst. Also, a simulation-based experiment is designed to indicate that the proposed management framework detects changes in workload intensity that occur over time and allocates multiple virtualized IT resources accordingly to achieve high availability and cost-effective targets.     

The architecture of virtual machines

A virtual machine can support individual processes or a complete system depending on the abstraction level where virtualization occurs. Some VMs support flexible hardware usage and software isolation, while others translate from one instruction set to another. Virtualizing a system or component -such as a processor, memory, or an I/O device - at a given abstraction level maps its interface and visible resources onto the interface and resources of an underlying, possibly different, real system. Consequently, the real system appears as a different virtual system or even as multiple virtual systems. Interjecting virtualizing software between abstraction layers near the HW/SW interface forms a virtual machine that allows otherwise incompatible subsystems to work together. Further, replication by virtualization enables more flexible and efficient and efficient use of hardware resources.     

An energy-aware virtual machines consolidation method for cloud computing: Simulation and verification

Cloud systems have become an essential part of our daily lives owing to various Internet-based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy-aware method for the VMC problem, which is called energy-aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non-functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.     

Large-scale virtual machines provisioning in clouds: challenges and approaches

The scale of global data center market has been explosive in recent years. As the market grows, the demand for fast provisioning of the virtual resources to support elastic, manageable, and economical computing over the cloud becomes high. Fast provisioning of large-scale virtual machines (VMs), in particular, is critical to guarantee quality of service (QoS). In this paper, we systematically review the existing VM provisioning schemes and classify them in three main categories. We discuss the features and research status of each category, and introduce two recent solutions, VMThunder and VMThunder+, both of which can provision hundreds of VMs in seconds.     

Energy consumption analysis for two embedded Java virtual machines

In this paper we present a general framework for estimating the energy consumption of an embedded Java virtual machine (JVM). We have designed a number of experiments to find the constant overhead and establish an energy consumption cost for individual Java opcodes for two JVMs. The results show that there is a basic constant overhead for every Java program, and that a subset of Java opcodes have an almost constant energy cost. We also show that memory access is a crucial energy consumption component.