云计算体系结构及应用实例分析

云计算包含两个方面的基本内容：一、描述用于构造应用程序的基础架构;二、描述建立在这种基础架构之上的应用和扩展服务;针对云计算的体系结构及应用实例,剖析其背后的技术含义以及当前云计算平台所采用的实现方法,进而评析当前云计算的发展状况,探讨实现云计算的技术方案。     

云计算新的基石 微软Dryad分布式并行计算平台解析

随着近几年云计算的飞速发展,“云”正在彻底改变用户现有的以桌面为核心的使用习惯,转向使用以Web为核心的存储与服务。对于准备提供云计算服务的企业来说,要使云计算真正地“落地”需要解决两个重要问题：如何构建与应用程序紧密结合的大规模底层基础设施？如何通过构建新型的云计算应用程序来提供更加丰富的用户体验？要解决这两个问题,采用专门的分布式并行计算平台是先决条件之一。     

OpenStack云平台的高可用设计与实现

实现高可用性是OpenStack云计算管理平台研究的重要问题之一。针对OpenStack云计算管理平台的相关服务组件运行在单节点上易导致单点故障(SPoF)的问题,结合现有多种系统高可用性解决方案,提出一种基于Pacemaker+Corosync＋HAProxy+Ceph的解决方案以实现OpenStack云计算管理平台的高可用。该方案将Active-Active的双活模式、Active-Passive的主备模式及集群技术3种高可用设计模式融合在一起,通过软硬件冗余和服务实例故障转移等方式实现OpenStack云计算管理平台的高可用性。实验证明,在少量节点或链路中断的情况下OpenStack云计算管理平台仍然能够稳定运行,该高可用性方案具有可行性。     

云计算系统研究及其在交通运输中的应用

云计算的高可扩展性、高可用性和廉价性吸引了越来越多的企业投入研究。在分析探讨几个典型的云计算平台架构的基础上,深入比较了各平台之间的差异,归纳总结了各平台现有的实例产品及应用案例。最后针对我国的交通运输发展现状以及交通信息数据处理的特性,提出了一种交通运输云平台参考体系,同时基于VMware云平台搭建了海量交通数据实验云平台并进行了计算性能测试分析。实验结果表明该平台能够在现有处理器、储存设备等基础设施不变的情况下整合智能交通系统现有的资源,提高整个交通运输系统的计算储存能力和数据安全性。     

一种基于Web的流媒体集群监控系统

本文提出了一种基于Web的流媒体集群监控系统。该系统实现了将离散的Helix流媒体服务器节点通过Web应用程序接口集中监控管理。此外,该系统作为集群监控系统,采用了灵活的模块化结构,并在LVS集群应用层的可靠性与容错方面做了不少工作。以上特性提高了集群系统的可扩展性和高可用性。     

基于DHT的拓扑感知节点聚集算法

在文件共享、流媒体和协作计算等P2P应用模型中,节点间采用单播通信并构建出对应的覆盖网络.由于覆盖网络通常建立在已有的底层网络之上,节点随机加入系统将导致上下层网络拓扑不匹配,不仅增加了节点间通信延时而且给底层网络带来较大的带宽压力.当前的拓扑匹配算法尚存在可扩展性低、节点聚集时延长等问题.在网络坐标算法和DHT算法基础之上,提出一种分布式的拓扑感知节点聚集算法TANRA,利用等距同心圆簇对节点二维网络坐标平面进行等面积划分,并根据节点所处区域进行多层命名空间中区间的一一映射.由于保留了节点之间的邻近关系,从而可使用DHT基本的"发布"和"搜索"原语进行相邻节点聚集.仿真结果表明,TANRA算法在大规模节点数时能有效保证网络拓扑匹配,并且具有较低的加入延时.     

IBM WAS ND一种典型集群架构的设计和实现

IBM WAS NP是一款符合J2EE标准的基础软件,是Java EE和 Web服务应用程序平台.它的集群功能为业务系统提供负载均衡、可扩展和高可用支持.一种典型集群架构包括1个Deployment Manager、2个应用程序服务器节点和2个Web服务器节点,通过Web服务器和应用程序服务器的灵活配置和扩展可提高集群...     

基于多核PC集群的并行绘制系统研究与实现

为满足大规模虚拟现实应用在渲染速度和显示分辨率等方面的要求,采用基于多核平台的PC集群系统,构建了高性价比的分布式图形渲染系统。系统充分结合多核PC集群中节点内部的并行和节点间的并行,通过对视景体的缩放和投影中心的移动实现了灵活的分屏,集群节点内部从渲染流水线、循环迭代、函数级三个层次进行了多核并行优化,有效地提高了并行绘制系统的效率。实验结果表明：多核平台与并行绘制系统结合,以多线程的方式有效地提高了应用程序性能。     

JBossCache缓存技术在集群系统中的应用

提出了一种利用JBossCache分布式缓存构建基于Java框架的集群环境下共享内存数据的思想,从提高应用程序的性能出发,给出了利用JBossCache开发电信级应用的解决方案。以移动公司利用JBossCache构建分布式缓存为实例,阐述了在集群环境下,缓存数据被自动复制,不用考虑集群的服务器之间的内存数据同步问题,各应用间快速共享内存数据的优势。JBossCache是基于Java的框架,使系统具有良好的可扩展性、高可用性和可移植性。     

云计算平台中的虚拟化技术与安全机制

随着云计算的普及,其安全问题变得越来越重要。本文通过研究存储虚拟化和网络虚拟化技术,设计并部署了一个弹性和安全的虚拟化集群。该集群利用虚拟化技术把云计算用户空间在逻辑上相互隔离,有效地解决了共享性和安全性之间的矛盾。该集群也具有良好的可扩展性、高可用性和较低的成本。     

Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time.     

基于SOA的云计算框架模型的研究与实现

云计算是一种能够向各种互联网应用提供硬件服务、基础架构服务、平台服务、软件服务、存储服务的系统,而SOA是一个组件模型,它将依靠各服务之间定义良好的接口和契约的应用程序联系起来。将云计算与SOA紧密地结合起来,形成一种基于SOA的云计算框架模型。实验证明,该模型简单、实用,充分体现了云计算与面向服务的架构的优势。     

浅谈云计算

云计算已经对IT界产生了十分重大的影响,是当前重要的研究领域。该文综述了当前云计算所采用的技术,剖析其背后的技术含义以及当前云计算参与企业所采用的云计算实现方案。通过此文可以了解云计算的当前发展状况以及未来的研究趋势。     

云计算下西部地区中小企业信息服务系统的分析

首先,介绍了西部地区中小企业信息化现状分析、企业云计算的发展的需求与动因、云计算特征与分类、云服务模式,其次,阐述了西部地区中小企业云信息服务平台的分析,涉及云信息服务系统体系架构、云平台软硬件资源分析、云基础设施管理平台数据关联访问及促进西部地区中小企业云电子商务应用等.     

Implementation and performance evaluation of a scheduling algorithm for divisible load parallel applications in a cloud computing environment

Cloud computing is an emerging technology in which information technology resources are virtualized to users in a set of computing resources on a pay‐per‐use basis. It is seen as an effective infrastructure for high performance applications. Divisible load applications occur in many scientific and engineering applications. However, dividing an application and deploying it in a cloud computing environment face challenges to obtain an optimal performance due to the overheads introduced by the cloud virtualization and the supporting cloud middleware. Therefore, we provide results of series of extensive experiments in scheduling divisible load application in a Cloud environment to decrease the overall application execution time considering the cloud networking and computing capacities presented to the application's user. We experiment with real applications within the Amazon cloud computing environment. Our extensive experiments analyze the reasons of the discrepancies between a theoretical model and the reality and propose adequate solutions. These discrepancies are due to three factors: the network behavior, the application behavior and the cloud computing virtualization. Our results show that applying the algorithm result in a maximum ratio of 1.41 of the measured normalized makespan versus the ideal makespan for application in which the communication to computation ratio is big. They show that the algorithm is effective for those applications in a heterogeneous setting reaching a ratio of 1.28 for large data sets. For application following the ensemble clustering model in which the computation to communication ratio is big and variable, we obtained a maximum ratio of 4.7 for large data set and a ratio of 2.11 for small data set. Applying the algorithm also results in an important speedup. These results are revealing for the type of applications we consider under experiments. The experiments also reveal the impact of the choice of the platforms provided by Amazon on the performance of the applications under study. Considering the emergence of cloud computing for high performance applications, the results in this paper can be widely adopted by cloud computing developers. Copyright © 2014 John Wiley & Sons, Ltd.     

Safeguarding Cloud Computing Infrastructure: A Security Analysis

Cloud computing is the provision of hosted resources, comprising software, hardware and processing over the World Wide Web. The advantages of rapid deployment, versatility, low expenses and scalability have led to the widespread use of cloud computing across organizations of all sizes, mostly as a component of the combination/multi-cloud infrastructure structure. While cloud storage offers significant benefits as well as cost-effective alternatives for IT management and expansion, new opportunities and challenges in the context of security vulnerabilities are emerging in this domain. Cloud security, also recognized as cloud computing security, refers to a collection of policies, regulations, systematic processes that function together to secure cloud infrastructure systems. These security procedures are designed to safeguard cloud data, to facilitate regulatory enforcement and to preserve the confidentiality of consumers, as well as to lay down encryption rules for specific devices and applications. This study presents an overview of the innovative cloud computing and security challenges that exist at different levels of cloud infrastructure. In this league, the present research work would be a significant contribution in reducing the security attacks on cloud computing so as to provide sustainable and secure services.     

云基础设施下的体系结构、挑战与机遇

在工业界和学术界的大力推动下,云计算作为一种新的服务模式,大致可分为将软件作为服务(Software as a service),将平台作为服务(Platform as a service),和将基础设施作为服务(Infrastructure as a Service).现有的绝大部分关于云计算的研究和讨论都集中在前两种服务.本文试图探讨云基础设施的体系结构及其面临的挑战和机遇.从冯.诺伊曼体系结构开始,计算机系统结构的研究基本上就可简单归类于三个问题:计算、存储与传输,三者相互影响.我们认为云基础设施也不例外.本文探讨了云计算的特点和优势,并从云体系结构的角度,探讨了云基础设施下的云计算、云存储和云传输所面临的挑战及其带来的可能的各种技术革命.     

多租户模式的业务平台云安全体系设计与实现

云计算技术目前已成为实现业务平台基础设施的可选方式之一。基于云计算技术,可以提供规模弹性、应用快速部署、资源按需分配与动念管理的业务平台云。但是在发展初期,云资源池主要以能力建设为主,对于安全体系的考虑尚不充分。通过对云资源池网络安全、应用安全及虚拟化安全等方面的分析和研究,给出了一种基于多租户模式的业务平台云安全体系,该方案已经广泛运用于中国电信业务平台云资源池,取得了显著的效果。     

New trends in parallel and distributed simulation: From many-cores to Cloud Computing

Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are happening at the ends of the computing spectrum: at the “small” scale, processors now include an increasing number of independent execution units (cores), at the point that a mere CPU can be considered a parallel shared-memory computer; at the “large” scale, the Cloud Computing paradigm allows applications to scale by offering resources from a large pool on a pay-as-you-go model. Multi-core processors and Clouds both require applications to be suitably modified to take advantage of the features they provide. Despite laying at the extreme of the computing architecture spectrum – multi-core processors being at the small scale, and Clouds being at the large scale – they share an important common trait: both are specific forms of parallel/distributed architectures. As such, they present to the developers well known problems of synchronization, communication, workload distribution, and so on. Is parallel and distributed simulation ready for these challenges? In this paper, we analyze the state of the art of parallel and distributed simulation techniques, and assess their applicability to multi-core architectures or Clouds. It turns out that most of the current approaches exhibit limitations in terms of usability and adaptivity which may hinder their application to these new computing architectures. We propose an adaptive simulation mechanism, based on the multi-agent system paradigm, to partially address some of those limitations. While it is unlikely that a single approach will work well on both settings above, we argue that the proposed adaptive mechanism has useful features which make it attractive both in a multi-core processor and in a Cloud system. These features include the ability to reduce communication costs by migrating simulation components, and the support for adding (or removing) nodes to the execution architecture at runtime. We will also show that, with the help of an additional support layer, parallel and distributed simulations can be executed on top of unreliable resources.