基于M/M/c休假排队模型的虚拟机调度策略

为了提高云系统的节能水平,同时保证用户的服务质量,提出了基于M/M/c休假排队理论的虚拟机(VM)调度策略.考虑到VM状态频繁切换对系统造成的损失,引入同步休眠和异步休眠相结合的休眠机制,同时设定休眠唤醒阈值以保证用户的服务质量.构建具有多服务台休假排队的二维Markov随机模型,运用拟生灭过程(QBD)和矩阵几何解等数学工具,给出了系统用户请求的平均延迟、处于各状态的平均VM数和空闲率等性能指标的解析式.最后通过数值实验验证了所提策略的有效性.     

基于虚拟机的多个安全级别的日志生成方法

该文的研究目的是提高日志在计算机系统中安全,为数据追踪和分析提供数据的修改情况的可靠依据。与数据类似,日志也有不同的安全级别(SL)。当不同权限的访问者做日志分析、审计等工作时,需要不同SL的日志,可以用多个虚拟机(VM)得到多个SL的数据文件日志。为此,根据数据文件受保护程度,把它们隔离存放在多个SL的工作VM内。当数据文件被访问时,用虚拟机监视器截取每个访问操作,并在日志VM内生成相应的日志。由于工作VM的SL不同,对应的日志VM的SL不同,所得到的日志的SL也各不相同。当VM之间共享文件或者由工作VM向日志VM传递数据时,访问控制模块按规则控制数据的流向。在带有sHype的虚拟机监视器xen的基础上所做实验表明:用多个VM生成多级安全日志的方法对数据文件访问性能的影响比较低。     

虚拟制造环境下有限元分析的应用

虚拟制造 ( VM)为产品设计和生产过程的优化提供了集成的建模和仿真环境。以设计为中心 VM在设计阶段为设计者提供了制造信息 ,它采用基于制造的仿真来优化产品和过程设计 ,从而实现制造目标。为此 ,提出了以设计为中心 VM系统的体系结构 ,分析了 VM环境下有限元分析的特点 ,并用 IDEF1 X方法描述了有限元分析的信息模型。基于以设计为中心 VM系统的集成环境 ,对某产品关键机械零件进行了有限元分析 ,优化了产品结构。结果表明 ,有限元分析与 VM系统的集成有效地减少了产品开发的时间和成本 ,提高了产品质量。     

利用FPGA的数字语音系统的设计与实现

采用基于AMBE算法的专用语音压缩编/解码芯片,设计并实现一种基于FPGA的语音压缩编/解码系统.整个控制程序包括复位及时钟分频程序、发送端控制程序和接收端控制程序3个功能模块,控制程序的接收端和发送端都使用先进先出(FIFO)电路存储器.仿真结果表明,控制程序能够完成相应的功能,符合设计的目的和要求.     

基于发布订阅系统的用户行为预测模型

基于iLBS系统中SP服务器的发布/订阅(pub/sub)中间件技术,提出借助于贝叶斯网络来预测用户行为的一种新颖的pub/sub模型(UBPM).新模式不仅以用户目前所在位置作为通告的唯一标准,而且考虑了用户环境信息中前后台信息的同步,并使用贝叶斯网络对移动用户的行为做出预测.因此,有效地解决了传统pub/sub系统中病态和冗余消息通告的问题,并提高了消息的精确性.实验结果表明相对于现有预测模型,UBPM预测模型更加有效.     

远程高性能计算环境的设计与实现技术

Ri CE(remote high computing environm ent)系统旨在建立以高性能并行计算机为计算资源的远程计算环境。系统合理划分本地机和远端机的工作任务 ,引入增量更新、压缩传送、检查点设置和恢复等多种传输措施 ,并集成了负载平衡工具 ,提供了身份检查、日志记录等安全机制 ,改变了传统的 Telnet工作模式。具有联网、远程执行过程对用户透明、系统安全性好和适应我国低带宽、实时性差、可靠性低的网络现状的特点。用户可以以较低的成本方便地使用远程计算机系统 ,从而充分发挥高性能计算机的性能。 Ri CE系统目前已在曙光 2 0 0 0和 IBM SP2等系统上运行通过 ,表明该系统性能良好 ,达到了系统设计目标     

基于互斥条件的云数据中心虚拟机整合策略

在云数据中心,虚拟机整合(VMC)是绿色计算和最小化集群功耗问题的关键技术。大多数研究采用基于资源使用率来寻求最合理的虚拟机(VM)整合方式,但在整合过程中并未考虑物理服务器(PM)的可用性及同位VM的稳定性问题。本文提出了基于互斥条件限制的VMC策略,该策略同时考虑PM的可用性和VM之间的互斥性两方面因素;给出了集群服务器统一资源预留计算方法,用于保证PM可用性的问题,并给出了基于VM历史运行数据的相似度判定方法,用于解决互斥条件的判定问题。将该策略应用于运行在OpenStack平台中的VM数据,实验结果表明:该策略可以有效地保证PM的可用性,避免同类型VM被整合在一起,减少同位VM的性能损失和确保服务质量。     

PC—8000微机系统EPROM写入器解译——兼论CP/M.O·S的易移植性

CP/M系Control Program For Microprocessor(微处理机控制程序)之简称.系一种单用户微机磁盘操作系统,它是由美国数学研制公司研制的,具有以下四种优点: 1)适用于以8080/8085/Z80为CPU的微机系统,而这些系统在市场上一直占优势; 2)CP/M结构清晰,层次分明,可靠性强,便于移植; 3)与I.B.M制兼容;     

基于ASP的网络化制造系统实施模式

为了解决网络化制造系统的实施难题,在对信息技术外包和应用服务提供商进行分析比较的基础上,提出了基于应用服务提供商(app lication serv ice prov ider,A SP)的网络化制造系统实施模式。给出了网络化制造系统的A SP关系图,详细介绍了关系图中独立软件供应商、硬件服务提供商、网络化制造课题组、政府部门、绍兴轻纺科技中心以及各个企业用户之间的具体关系;然后给出了一个A SP服务的用户访问控制模型,以及一个根据用户类别、用户规模、服务类型、使用服务次数、占用资源数量等因素来确定的A SP费用模型;最后结合绍兴轻纺区域网络化制造系统对该模式的应用进行了说明。这个基于A SP的网络化制造系统实施模式可以很好地指导系统的实施,并能很好地拓展以应用于其他典型经济区域。     

云环境下顾及SLA及QoS的资源调度策略

考虑到云技术是当前较为流行基于服务的分布式计算及其不需要用户关注底层的系统实现。有效的资源调配一方面能减少资源的过度浪费,或者减少成本以增加用户的满意度,最终提升系统的性能。本文通过对云计算平台资源的虚拟化技术,实现系统性能需求的精确预算。从服务的角度,探讨一种云环境下基于Virtual Machine(VM)的顾及Service Level Agreement(SLA)及Quality of Service(QoS)的资源调度策略。模拟实验结果表明,本资源的调度策略是一种提高系统资源利用率的有效手段,具有一定的实用价值。     

I-Neat:An Intelligent Framework for Adaptive Virtual Machine Consolidation

With the increasing use of cloud computing, high energy consumption has become one of the major challenges in cloud data centers. Virtual Machine(VM) consolidation has been proven to be an efficient way to optimize energy consumption in data centers, and many research works have proposed to optimize VM consolidation.However, the performance of different algorithms is related with the characteristics of the workload and system status;some algorithms are suitable for Central Processing Unit(CPU)-i...     

A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems

Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM) management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.     

Trusted Attestation Architecture on an Infrastructure-as-a-Service

Trusted attestation is the main obstruction preventing large-scale promotion of cloud computing.How to extend a trusted relationship from a single physical node to an Infrastructure-as-a-Service(IaaS) platform is a problem that must be solved.The IaaS platform provides the Virtual Machine(VM),and the Trusted VM,equipped with a virtual Trusted Platform Module(vTPM),is the foundation of the trusted IaaS platform.We propose a multi-dimensional trusted attestation architecture that can collect and verify trusted attestation information from the computing nodes,and manage the information centrally on a cloud management platform.The architecture verifies the IaaS's trusted attestation by apprising the VM,Hypervisor,and host Operating System's(OS) trusted status.The theory and the technology roadmap were introduced,and the key technologies were analyzed.The key technologies include dynamic measurement of the Hypervisor at the process level,the protection of vTPM instances,the reinforcement of Hypervisor security,and the verification of the IaaS trusted attestation.A prototype was deployed to verify the feasibility of the system.The advantages of the prototype system were compared with the Open CIT(Intel Cloud attestation solution).A performance analysis experiment was performed on computing nodes and the results show that the performance loss is within an acceptable range.     

Cloud Virtual Machine Lifecycle Security Framework Based on Trusted Computing

As a foundation component of cloud computing platforms, Virtual Machines(VMs) are confronted with numerous security threats. However, existing solutions tend to focus on solving threats in a specific state of the VM. In this paper, we propose a novel VM lifecycle security protection framework based on trusted computing to solve the security threats to VMs throughout their entire lifecycle. Specifically, a concept of the VM lifecycle is presented divided up by the different active conditions of the VM. Then, a trusted computing based security protection framework is developed, which can extend the trusted relationship from trusted platform module to the VM and protect the security and reliability of the VM throughout its lifecycle. The theoretical analysis shows that our proposed framework can provide comprehensive safety to VM in all of its states. Furthermore, experiment results demonstrate that the proposed framework is feasible and achieves a higher level of security compared with some state-of-the-art schemes.     

虚拟制造及其应用

从产品生命周期着手,阐明虚拟产品、虚拟制造（ＶＭ）技术的概念,比较和总结了ＶＭ与其他先进制造技术之间的关系．解释ＶＭ的过程和体系结构,并从例举的应用实例中提出ＶＭ的关键技术．对有关ＶＭ的软硬件和研究情况作了介绍和分析,并提出ＶＭ研究的方向．     

Load Feedback-Based Resource Scheduling and Dynamic Migration-Based Data Locality for Virtual Hadoop Clusters in OpenStack-Based Clouds

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service(Iaa S) cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on Iaa S(DHCI) architecture, which includes four key modules: monitoring,scheduling, Virtual Machine(VM) management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host(s) or rack(s) where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on Open Stack.Substantial experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.     

PVM特性

目前 ,计算机应用越来越复杂 ,单台微机已经不满足需要。因此 ,基于网络机群的并行处理引起了许多专家的关注。PVM(ParallelVirtualMachine)是一种消息传递库。它为用户提供了一个并行处理环境。本文详细介绍了PVM的消息传递库及其特性     

用于Web计算的Java虚拟机嵌入式系统设计

传统的Java虚拟机在工作的时候,需要占用大量的系统资源.因此,小型嵌入式Java虚拟机的设计是很必要的.总的来讲,嵌入式系统在有限的资源中需要一种实时的计算.本文对传统的Java虚拟机进行了适当的修正,使得它在嵌入式系统中工作时基本上达到了实时的要求.     

Architecture of Virtual Manufacturing based on Autonomous Manufacturing Islands

Virtual manufacturing (VM) is an integrated synthetic manufacturing environment exercised to enhance all levels of decision and control in a manufacturing networked enterprise. Autonomous manufacturing island (AMI) is an active, dynamic and flexible production cell in the domain of technology, organization and management. This paper explains the concept of VM, proposes the architecture of VM based on AMI and discusses its characteristics. Lastly, a pilot system of virtual manufacturing based on AMI is introduced.     

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine(VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service(Qo S). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem(LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants' overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.