基于存储和计算分离的NHD系统*

介绍的NHD(Network Hard Disk)系统,在计算资源和存储资源物理分离的基础上,根据不同应用的需求,通过在客户端提供的一种特殊硬件设备将两者动态重组以构建新的计算机系统.这样形成的计算环境具有动态性、个性化和便于管理等特点.     

一种动态连接存储资源和计算资源装置的设计与实现

存储与计算的分离，重新定义了计算机的使用模式。计算资源和存储资源的动态重构，不仅提高了资源的利用率，而且简化了管理。一种实现这种新模式同时又兼容于传统机制的方案是在存储设备级截获数据流，并转接到网络，利用现在网络高带宽、可靠和灵活的特性提供高速、稳定和动态的服务。本文将介绍动态网络硬盘nHD（network HardDisk）的设计和实现，给出并分析其运行性能。     

异构应用资源共享系统的研究与设计

随着应用资源的日益增长和应用需求的不断变化，个人计算环境需要集成不同平台的异构应用资源。该文基于虚拟计算技术，提出一种异构应用资源融合和共享机制。通过对应用资源的挖掘、发布及注册，实现跨平台的异构应用资源融合和个人计算环境的动态重构。研究成果在科技部科技基础条件平台建设计划中得到了较好的应用。     

名词解释：网络计算机

网络计算机（Network Computer)：简称NC，是一种基于网络计算环境，便于集中管理的信息处理和信息访问终端设备。它能够通过网络获取服务器端的计算、应用和存储资源，同时拥有一定的本地计算能力。网络计算机是连接到网络上的终端设备，也可以称之为网络终端机(Network Client)。网络计算机通过网络访问服务器端的应用、计算和存储资源，同时充分利用本地的计算资源，设备资源和人力交互机制。     

广域分布式环境中面向服务的文件管理系统

在广域分布式环境中,文件资源存储在分布、异构的环境下并通过不同协议被访问.该文针对广域分布式环境和数据密集型应用的需求提出一个面向服务的文件管理系统.该系统把分布、异构的存储资源用虚拟技术封装成一个文件存储资源,并提供统一的文件视图与访问接口,实现存储资源的共享.实际测试结果表明了该系统的有效性.     

面向分布对象存储结构的高性能计算系统资源管理方法

当前的高性能计算系统的资源管理和调度关注的焦点是计算资源,然而随着高性能计算系统的规模增大和计算能力增强,其I/O瓶颈问题日益突出.由于高性能计算系统的存储结构多样性带来了存储资源管理分配的难题,在目前主流的资源管理系统中尚未有针对I/O存储资源的调度和管理.随着对象存储结构的发展和广泛使用,大多数主流高性能系统采用分布对象存储系统,研究对分布对象存储系统的管理并结合资源管理系统,实现面向存储的作业优化调度,对提升高性能计算系统的实际性能有重要意义.针对具有分布对象存储结构的高性能计算系统,研究面向分布存储的资源管理方法,在作业调度和资源分配时考虑不同应用的I/O需求,通过建立分布对象存储资源模型和应用程序I/O能力需求模型,并在资源调度和分配上根据不同的I/O应用级别,为作业分配合适的存储资源,设计并实现基于I/O能力分级的作业调度和资源分配算法.系统测试表明:该方法可以显著提高多作业环境下应用的性能,保证应用程序的性能稳定性,提高系统的吞吐率.     

基于Openstack的高能物理虚拟计算集群系统及应用

高能物理计算是典型的高性能计算的应用,运行时需要大量的CPU资源。如果系统的CPU资源利用率不高,会使得计算效率大大下降。传统的高能物理计算环境资源管理是静态的,很难同时满足突发、批处理、CPU密集型、数据密集型等不同类型的作业对于不同的物理资源的需求。文中基于Openstack构建的虚拟计算集群系统,实现以CPU核为粒度进行调度作业,根据当前的作业和虚拟资源情况,动态调度资源,大大提高了资源的利用率。首先介绍本系统的相关研究工作,包括KVM虚拟机的测试优化、高能物理作业在虚拟机上的性能测试及高能物理公共服务云IHEPCloud,这些工作进一步表明了高能物理实验的数据分析在虚拟机上的性能是完全可以被接受的;然后详细介绍了虚拟计算集群系统的设计与实现;最后给出虚拟机计算集群在高能物理计算中的实际应用情况,证明了虚拟计算集群系统能很好地满足高能物理的计算需求。     

高能物理云平台中的弹性计算资源管理机制

虚拟化技术作为一种新的资源管理技术,正在高能物理领域得到越来越广泛的应用。静态虚拟机集群方式已经逐渐不能满足多作业队列对于计算资源动态的需求。为此,实现了一种云计算环境下面向多作业队列的弹性计算资源管理系统。系统通过高吞吐量计算系统HTCondor运行计算作业,使用开源的云计算平台Openstack管理虚拟计算节点,给出了一种结合虚拟资源配额服务,基于双阈值的弹性资源管理算法,实现资源池整体伸缩,同时设计了二级缓冲池以提高伸缩效率。目前系统已部署在高能所公共服务云IHEPCloud上,实际运行结果表明,当计算资源需求变化时系统能够动态调整各队列虚拟计算节点数量,同时计算资源的CPU利用率相比传统的资源管理方式有显著的提高。     

负载平衡的多级并行对等计算在新药研发网格中的实现

介绍了一种在Internet环境下利用分布的计算资源实现计算/数据密集的高通量新药筛选的应用网格——新药研发网格(drug discovery grid，DDG)．对DDG中采用的多级并行对等计算模型(P2P)，自适应的动态负载平衡算法及实现，网格资源的容错技术以及系统安全做了较详细的描述，给出了一组实际环境下的实验数据．实验证明，DDG具有很好的负载平衡能力和良好的系统健壮性，新药筛选应用在大规模的互连网环境下可以获得良好的并行加速效果．     

一种面向服务的动态协同架构及其支撑平台

为了让面向服务的架构下的应用系统能够灵活地动态演化以适应底层因特网计算环境和用户需求的变化,该文提出了一种面向服务的动态协同架构．该架构引入内置的运行时体系结构对象来解耦系统中的各个服务构件,并通过该对象以体系结构的视角来重解释服务部件之间的引用和交互．这样就把体系结构这一抽象概念具体化为可直接操控的对象,从而可以利用面向对象程序设计语言的继承和多态等整套机制,导出一种面向体系结构的系统动态演化技术．为支持这一架构,设计并实现了一个支撑平台Artemis-ARC,为具有动态调整能力的面向服务应用系统的开发、运行和监控提供了一套可视化的集成环境．在此平台上还开发了一个简单的示例应用以展示动态调整的效果．     

Qos-aware mobile service optimization in multi-access mobile edge computing environments

With the rapid development of mobile Internet technologies and various new service services such as virtual reality (VR) and augmented reality (AR), users’ demand for network quality of service (QoS) is getting higher and higher. To solve the problems of high load and low latency in-network services, this paper proposes a data caching strategy based on a multi-access mobile edge computing environment. Based on the MEC collaborative caching framework, an SDN controller is introduced into the MEC collaborative caching framework, a joint cache optimization mechanism based on data caching and computational migration is constructed, and the user-perceived time-lengthening problem in the data caching strategy is solved by a joint optimization algorithm based on an improved heuristic genetic algorithm and simulated annealing. Meanwhile, this paper proposes a multi-base station collaboration-based service optimization strategy to solve the problem of collaboration of computation and storage resources due to multiple mobile terminals and multiple smart base stations. For the problem that the application service demand in MEC server changes due to time, space, requests and other privacy, an application service optimization algorithm based on the Markov chain of service popularity is constructed, and a deep deterministic strategy (DDP) based on deep reinforcement learning is also used to minimize the average delay of computation tasks in the cluster while ensuring the energy consumption of MEC server, which improves the accuracy of application service cache updates in the system as well as reducing the complexity of service updates. The experimental results show that the proposed data caching algorithm weighs the cache space of user devices, the average transfer latency of acquiring data resources is effectively reduced, and the proposed service optimization algorithm can improve the quality of user experience.     

云计算环境下的网络安全技术

云计算机具有强大的数据处理功能,能够满足安全高效信息服务及高吞吐率信息服务等多种应用需求,且可以根据需要对存储能力与计算能力进行扩展。但在网络环境下云计算机同样面临许多网络安全问题,因此本文对云计算机环境下常见的网络安全问题进行了探讨,包括拒绝服务安全攻击、中间人安全攻击、SQL注入安全攻击、跨站脚本安全攻击及网络嗅探;同时分析了云计算机环境下的网络安全技术,包括用户端安全技术,服务器端安全技术,用户端与云端互动时的安全技术。     

桌面网格环境下遥感数据动态存储模式研究

基于网格体系结构节点资源能够被共享并被协同使用的概念,设计在分布式节点上实现数据存储和传递的网格数据存储系统.该系统允许网格用户在本地将数据上传到网格,网格管理节点负责对参与共享存储资源的节点进行管理,并将预定大小的数据分配到相应节点存储,同时响应网格用户的请求,使用基于Hash表的路由信息,找到对应请求网格数据的最佳路径,并激活网格线程,实现网格数据在节点间的完整传递.基于Alchemi网格中间件和.NET框架对遥感数据而进行的开发和应用表明,桌面网格动态存储是一个可行的网格计算应用.     

云计算环境下Web服务集成系统的研究

作为云平台提升应用性能的一种重要手段,Web服务集成技术近年来受到了工业界和学术界的广泛关注。从云计算与Web服务集成技术的结合入手,分析设计了基于云计算的系统体系结构,并基于此提出了基于Web Ser-vices的异构数据集成方法和应用集成的总体架构。最后,给出了该系统的相关实现实例。实验表明,该系统架构的应用在降低构建成本的同时大幅提高了系统性能。     

磁盘阵列技术及其选择

存储技术一直以来是计算机技术的热点问题.为了满足用户对存储数据的安全、存取速度快和超大存储容量的需求,磁盘阵列技术得到了迅速发展.通过介绍磁盘阵列技术概念和级别,进一步对不同级别的磁盘阵列性能进行比较,以帮助用户选择合适的磁盘阵列产品.     

Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing

The latest developments in mobile computing technology have increased the computing capabilities of smartphones in terms of storage capacity, features support such as multimodal connectivity, and support for customized user applications. Mobile devices are, however, still intrinsically limited by low bandwidth, computing power, and battery lifetime. Therefore, the computing power of computational clouds is tapped on demand basis for mitigating resources limitations in mobile devices. Mobile cloud computing (MCC) is believed to be able to leverage cloud application processing services for alleviating the computing limitations of smartphones. In MCC, application offloading is implemented as a significant software level solution for sharing the application processing load of smartphones. The challenging aspect of application offloading frameworks is the resources intensive mechanism of runtime profiling and partitioning of elastic mobile applications, which involves additional computing resources utilization on Smart Mobile Devices (SMDs). This paper investigates the overhead of runtime application partitioning on SMD by analyzing additional resources utilization on SMD in the mechanism of runtime application profiling and partitioning. We evaluate the mechanism of runtime application partitioning on SMDs in the SmartSim simulation environment and validate the overhead of runtime application profiling by running prototype application in the real mobile computing environment. Empirical results indicate that additional computing resources are utilized in runtime application profiling and partitioning. Hence, lightweight alternatives with optimal distributed deployment and management mechanism are mandatory for accessing application processing services of computational clouds.     

基于平行因子分解的频域盲解卷积算法

针对卷积混合盲分离问题,文章提出了一张基于张量平行因子分解的盲分离算法。该算法通过将接收信号的频域相关矩阵叠加成三阶张量,再对此三阶张量进行平行因子分解,最后利用基于K-means聚类的全排列解模糊算法来完成无排列模糊的混合矩阵估计。通过仿真实验,计算分离信号与源信号的相似系数,结果表明提出的算法具有很好的分离效果,而且实现简单,可满足实际应用的要求。     

Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows.     

云计算环境下的弹性结构程序研究

针对云计算环境下云程序的弹性结构进行研究,结合Anytime算法、弹性计价模型和软件即服务层中的SLA计费架构,将总业务费用分为接入费用、使用费用和补偿费用进行计算,设计基于图像小波变换的弹性结构程序,给出程序的执行步骤和状态转换图。分析结果表明,该弹性结构程序能对程序迭代次数进行控制,较好满足用户需求。