2018政务云解决方案提供商TOP20

＂云＂时代,政务云也迎来发展期身处信息化时代,人们对＂云＂这个词越来越熟悉。＂环保云＂、＂医疗云＂、＂政务云＂等云平台逐渐走进人们视野。＂云＂在这里只是一个形象的说法,借用云所具有的开放性、可扩展性、灵活性等特征,比喻＂云＂中的资源对用户来说可以随时获取、按需选用。云计算已经发展成为数字化转型时代的关键支撑技术,＂上云＂成为大势所趋下的必然选择。     

移动机房新观念,你懂么？——实景体验APC Mini-英飞移动机房解决方案

你知道么？移动互联的迅速普及,不仅加速演变着我们每个人的工作、生活,更在不为人知的背后,推动了企业计算架构的重大变革。特别是当越来越多的企业,将业务信息系统迁移至＂云＂端后,一场企业计算基础设施架构的颠覆性裂变正在悄然蔓延,你感受到了么？来自云端的魅惑：企业,你挡不住的确,随着云技术的逐渐成熟,无论是成熟的大、中型企业,还是新兴的小、微企业,甚至是一些个体的创业团队,越来越多的企业深刻地体会到了来自＂云＂端服务的便捷、高效、实惠、可靠。     

Chif m资料管理和即时通讯体验＂有道云协作＂

个人应用市场竞争的硝烟还未散去,互联网巨头们对企业级应用市场的野心崭露头角。继微信发布企业号之后,近日网易也高调宣布进军企业级应用市场,这一次,网易凭借的是一款全新的企业协作应用——有道云协作。网易公司创始人兼CEO丁磊在发布会上表态——＂有道云协作会是企业员工真正喜欢的企业级应用＂。有道云协作是一款以＂团队资料管理＂为核心,重视＂即时通讯＂功能,支持多平台互动的企业级应用。     

跨平台的IaaS层资源池管理系统的设计与实现

云计算产业正在呈现多元化，多层次的发展趋势，企业对云计算资源的需求也从单一的云平台转变为多种云平台结合使用。为了解决企业数字化业务在上云过程中的差异化问题，本文提出实现一套跨平台的IaaS层资源池管理系统，将多种云计算资源进行整合，包括公有云、私有云、本地物理服务器等。屏蔽底层云计算资源技术上的差异，对上层系统提供统一的服务，能够对企业业务提供有力的支撑，加快企业的数字化转型。     

多关键字云资源搜索算法

云计算的核心是在虚拟化技术的基础上,通过互联网技术为用户提供动态易扩展的计算资源。利用中心服务器的计算模式来管控网络上大量云资源使得中心服务器成为整个系统的瓶颈,不利于云计算的大规模应用,因此提出使用对等网络技术构建分布式的云资源索引存储和查询系统,但是结构化拓扑系统维护比较复杂,一般不支持复杂搜索条件查询。本文提出了一种多关键字云资源搜索算法。在基于分层超级节点的云资源搜索算法基础上进行路由算法改进,希望实现多关键字的精确查询。对多关键字的生成、分割及存储做出了详细说明,提出一种有效的基于数据集的索引搜索策略,实现了包含三个或三个以上的关键字高效、准确查询。分析实验结果证明了算法明显提高了资源搜索的命中率,尤其是随着关键字数目的增多,不仅保证了资源搜索的命中率,同时大大增加了资源的召回率。     

以简驭繁 联想扬天S700

从传统分体式结构的A系列到一体化设计的S系列,联想扬天的桌面办公平台虽然向着简约化的方向在发展,但是它承载的任务却是日益繁复。在＂一键杀毒＂、＂闪电恢复＂、＂企业在线服务＂等个性化的应用之后,联想扬天又给用户带来了云服务平台的概念。从一款单纯的硬件计算平台,到整合了多种优质资源的服务终端,联想扬天S700一体式电脑向中小企业用户深刻地诠释了以简驭繁之道。     

为“云”集成

产品背景：＂复杂IT＂日益成为企业在推动IT转型之路上的一大阻碍,IT行业正在互换更具协同性的集成解决方案,可以让企业的IT基础架构更灵活、更具成本效益。甲骨文自2008年推出第一款Exadata数据库云服务器以来,甲骨文系统家族日益丰富和强大,无论是Exadata数据库云服务器、Exalogic中间件云服务器、Exalytics商务智能云服务器、大数据机、虚拟计算一体机,还是Oracle Super Cluster等甲骨文集成产品,     

虚拟化技术OpenStack在高性能计算集群上的应用与研究

本文以高性能计算集群为基础,利用开源虚拟化技术OpenStack,研究虚拟化技术在高性能计算集群上的应用,以解决资源受架设方式、地域或物理组态所限制的问题。利用虚拟化技术架构的优越性有效的降低高性能计算集群能耗,提高资源的利用效率和云实验室资源的容灾能力。     

规划云计算的规模——云中的扩容

云,让你能够根据负载需求调整计算资源。你可以手动地调整计算能力（通过命令行或web界面）,也可以通过编程来调整（或者用预先定义的调整模式,或者使用能自动调整计算能力以适应实际需要的软件）。     

面向云测试的主机预测技术

云测试用于云上管理和执行测试任务。由于云上的计算资源能够较为方便的扩展，而负载测试通常需要大量计算资源，因此负载测试非常契合基于云的测试方法。为了保证负载测试能够按照测试计划成功发起，并且不会出现资源不足或闲置，需要在测试执行前确定云上虚拟主机数目，而现有的研究还缺少此类技术。为此，提出了一种面向脚本化负载测试的云主机预测技术，在负载测试开始前执行小规模的负载测试，收集计算资源与测试负载的基础数据，在此基础上使用机器学习方法进行数据训练并预测主机数量。结果表明：提出的主机预测方法能够较好地预测出负载测试所需主机数目。     

Sky Computing

Infrastructure-as-a-service (IaaS) cloud computing is revolutionizing how we approach computing. Compute resource consumers can eliminate the expense inherent in acquiring, managing, and operating IT infrastructure and instead lease resources on a pay-as-you-go basis. IT infrastructure providers can exploit economies of scale to mitigate the cost of buying and operating resources and avoid the complexity required to manage multiple customer-specific environments and applications. The authors describe the context in which cloud computing arose, discuss its current strengths and shortcomings, and point to an emerging computing pattern it enables that they call sky computing.     

Resource Management in Cloud Computing with Optimal Pricing Policies

As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue by means of optimal pricing schemes in what is a largely monopolized cloud market. We first study how the revenue of a cloud provider can be maximised using an on-demand pricing scheme. We then turn to the study of revenue maximization with a reserved pricing scheme and, finally, we compare the revenues obtained from the two pricing schemes.     

Optimization of cloud computing task execution time and user QoS utility by improved particle swarm optimization

In order to optimize the quality of service (QoS) and execution time of task, a new resource scheduling based on improved particle swarm optimization (IPSO) is proposed to improve the efficiency and superiority. In cloud computing, the first principle of resource scheduling is to meet the needs of users, and the goal is to optimize the resource scheduling scheme and maximize the overall efficiency. This requires that the scheduling of cloud computing resources should be flexible, real-time and efficient. In this way, the mass resources of cloud computing can effectively meet the needs of the cloud users. Field Programmable Gate Arrays (FPGA), high performance and energy efficiency in one field. Most of them would have been the particle algorithm. The current technological development is still in-depth at super-resolution image research at an unprecedentedly fast pace. In particular, systemic origin applications get a lot of attention because they have a wide range of abnormal results. The scientific resource scheduling algorithm is the key to improve the efficiency of cloud computing resources distribution and the level of cloud services. In addition, the physical model of cloud computing resource scheduling is established. The performance of the IPSO algorithm applied to cloud computing resource scheduling is analysed in the design experiment. The comparison result shows that the new algorithm improves the PSO by taking full account of the user's Qu's requirements and the load balance of the cloud environment. In conclusion, the research on cloud computing resource scheduling based on IPSO can solve the problem of resource scheduling to a certain extent.     

基于Wi-Fi与Web的云计算资源调度算法研究

为缩短云计算执行时间,改善云计算性能,在一定程度上加强云计算资源节点完成任务成功率,需要对云计算资源进行调度;当前的云计算资源调度算法在进行调度时,通过选择合适的调度参数并利用CloudSim仿真工具,完成对云计算资源的调度;该算法在运行时无法有效地进行平衡负载,导致云计算资源调度的均衡性能较差,存在云计算资源调度结果误差大的问题;为此,提出一种基于Wi-Fi与Web的云计算资源调度算法;该算法首先利用自适应级联滤波算法对云计算资源数据流进行滤波降噪,然后以降噪结果为基础,采用本体论对云计算资源进行预处理操作,最后通过人工蜂群算法完成对云计算资源的调度;实验结果证明,所提算法可以良好地应用于云计算资源调度中,有效提高了云计算资源利用率,具有实用性以及可实践性,为该领域的后续研究发展提供了可靠支撑。     

移动边缘计算环境下的动态资源分配策略

在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。     

浅议云计算的实现及未来的发展

所谓云计算其实是是一种基于因特网的超级计算模式,在远程云计算的数据中心,几万甚至几千万台电脑和服务器连接成一片。云计算的核心思想,是将大量用网络连接的计算资源统一管理和调度,构成一个计算资源池向用户按需服务。提供资源的网络被称为“云”。用户通过电脑、笔记本、手机等方式接入数据中心,按各自的需求进行存储和运算。本文主要介绍了云计算的概念以及将来的发展。     

基于云计算的开放性教学资源平台建设研究

针对当前高等教育信息化资源平台建设零散分布、重复投资等突出问题,提出了基于云计算的开放性教学资源平台建设方案。以期建立一个基于云计算的集中管理、分散存储的资源开发、管理、应用系统平台,形成高等教育信息化资源共建共享的机制。首先分析了基于云计算的开放性教学资源平台建设的可行性,然后提出了基于云计算的开放性教学资源平台的设计思想、系统设计、系统架构、资源库建设内容,并对主要应用功能进行了概述,有助于推进基于云计算的高校信息化资源平台建设进程。     

Cloud-based architecture for web applications with load forecasting mechanism: a use case on the e-learning services of a distant university

In cloud systems, a clear necessity emerges related to the use of efficient and scalable computing resources. For this, accurate predictions on the load of computing resources are a key. Thanks to these accurate predictions, reduced power consumption and enhanced revenue of the system can be achieved, since resources can be ready when users need them and shutdown when they are no longer needed. This work presents an architecture to manage web applications based on cloud computing, which combines both local and public cloud resources. This work also presents the algorithms needed to efficiently manage such architecture. Among them, a load forecasting algorithm has been developed based on Exponential Smoothing. An use case of the e-learning services of our University presenting the behaviour of our architecture has been evaluated through a series of simulations. Among the most remarkable results, power consumption is reduced by 32 % at the cost of 367.31 US$ a month compared with the current architecture.     

Outlook: Cloudy with a Chance of Security Challenges and Improvements

Cloud computing presents an opportunity to offload computing to third party resources, but this business model isn't without security risks. Customers must determine if running their computing on a base system managed by a third party is better than running on their own systems. They must be convinced that running in the same data center as their competitors or adversaries is safe, and they are still responsible for complex security configuration. Despite these concerns, cloud vendors can also leverage the cloud architecture to improve security by building verifiable base systems, designing integrity-protected cloud services, and limiting security configuration complexity. Thus, while the cloud architecture provides security challenges, there are also opportunities to ease the security configuration burden on customers.     

基于云的计算机取证系统研究

云计算是目前最流行的互联网计算模式,它具有弹性计算、资源虚拟化、按需服务等特点。在云计算的环境中,云计算中心直接提供由基础设施、平台、应用等组成的各种服务,用户不再拥有自己的基础设施、软件和数据,而是共享整个云基础架构。这种方式直接影响了云计算环境的安全性和可用性,给云计算带来了巨大的隐患。在分析和研究云计算环境下的安全缺陷和安全威胁的前提下,提出了一种基于云的计算机取证系统的设计方案———利用计算机取证技术解决云计算环境的安全问题,同时利用云计算技术和超算技术满足传统取证对高性能计算的需求。