Cloud Computing Technology and Its Applications

Virtualization and distributed parallel architecture are typical cloud computing technologies. In the area of virtuatization technology, this article discusses physical resource pooling, resource pool management and use, cluster fault location and maintenance, resource pool grouping, and construction and application of heterogeneous virtualization platforms. In the area of distributed technology, distributed file system and KeyNalue storage engine are discussed. A solution is proposed for the host bottleneck problem, and a standard storage interface is proposed for the distributed file system. A directory-based storage scheme for Key/Value storage engine is also proposed.

Critical Factors for Personal Cloud Storage Adoption in China

Purpose： In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user＇s intentions.Design/methodology/approach： Based on technology acceptance model（TAM）, network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire.Findings： Among the adoption factors we identified, network externality has the salient influence on a user＇s adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage.Research limitations： This study ignores time and cost factors that might affect a user＇s intention to adopt personal cloud storage.Practical implications： Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies.Originality/value： This study is one of the first research efforts that discuss Chinese users＇ personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.

Performance Analysis and Framework Optimization of Open Source Cloud Storage System

More and more embedded devices, such as mobile phones, tablet PCs and laptops, are used in every field, so huge files need to be stored or backed up into cloud storage. Optimizing the performance of cloud storage is very important for Internet development. This paper presents the performance evaluation of the open source distributed storage system, a highly available, distributed, eventually consistent object/blob store from Open Stack cloud computing components. This paper mainly focuses on the mechanism of cloud storage as well as the optimization methods to process different sized files. This work provides two major contributions through comprehensive performance evaluations. First, it provides different configurations for Open Stack Swift system and an analysis of how every component affects the performance. Second, it presents the detailed optimization methods to improve the performance in processing different sized files. The experimental results show that our method improves the performance and the structure. We give the methods to optimize the object-based cloud storage system to deploy the readily available storage system.

Storage System Design Scheme in Virtualization Construction

In order to improve resource utilization, it is necessary to integrate storage and data, and the emergence of cloud computing makes it possible. This paper analyzed the study of virtualization and cloud computing, proposed a new scheme based on virtualization, and established a shared storage platform, which made a good complement and perfected the centralized storage platform.

Experimental Study on Cloud-Computing-Based Electric Power SCADA System

With the development of smart grid, the electric power supervisory control and data acquisition （SCADA） system is limited by the traditional IT infrastructure, leading to low resource utilization and poor scalability. Information islands are formed due to poor system interoperability. The development of innovative applications is limited, and the launching period of new businesses is long. Management costs and risks increase, and equipment utilization declines. To address these issues, a professional private cloud solution is introduced to integrate the electric power SCADA system, and conduct experimental study of its applicability, reliability, security, and real time. The experimental results show that the professional private cloud solution is technical and commercial feasible, meeting the requirements of the electric power SCADA system.

Research on Building the Resource Library of Course Teaching based on the Cloud Computing

This paper puts forward sharing teaching resources based on cloud computing solutions, through the use of architecture means virtualization technology based on KVM on the server side, the infrastructure layer manage the underlying physical hardware equipment. In the realization of the infrastructure layer using Libvirt virtualization management suite that provides a common API development Web, through the RDP protocol, and finally access to the remote virtual desktop browser by the graphical user interface （GUI） and traditional Web B/S architecture, to simulate and access to low-level resources and sharing of teaching resources, teaching resources can be achieved education informatization in the process of teaching.

Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

A Survey on Cloud Security

Cloud computing system packages infrastructures,applications and other resources as services,and delivers the services to market in an elastic and fast way.The significant advantages of cloud computing,e.g.,scalability,elasticity,and pay-per-use,bring it considerable commercial values.Nevertheless,owing to the new application scenario,e.g.,multi-tenant,cloud computing is encountering potential security risks.This paper reviews the state-of-art research in cloud security.According to the attack levels,it analyzes four kinds of attacks in the cloud,i.e.,network-based attacks,VM-based attacks,storage-based attacks,and application-based attacks.The countermeasures and corresponding techniques are then introduced.Furthermore,this paper also discusses an innovative and promising solution for cloud security by dynamically changing system configuration.

基于数字孪生技术的云储能点对点交易双层优化策略

为解决云储能(Cloud energy storage, CES)日前交易模型预测精度低、信息传输不及时、交易机制可靠性差等问题,提出一种基于数字孪生技术的云储能点对点交易双层优化策略。首先,针对环境因素的时变性,采用数字孪生(Digital Twins, DT)技术实现新能源电厂出力与负荷的超短期预测;其次,考虑到用户规模不断增大,构建以云储能供应商-社区-用户为体系的改进分布式点对点(Peer-to-peer, P2P)交易策略,实现交易的去中心化;再次,为同时满足CES供应商与用户两大主体的利益需求,建立基于日内滚动优化的双层优化配置模型,上层求解储能电站的优化配置,下层求解用户用电策略,并采用Karush-Kuhn-Tucher(KKT)条件与Big-M法将多目标双层非线性优化问题转化为单层线性优化问题;最后,通过两次使用shapley值计算社区与用户成本分摊。算例仿真结果表明,所提策略能够实现新能源电厂出力与负荷的高精度预测,降低用户群与CES供应商交易的复杂度,在用户购电成本最小的基础上最大化CES供应商的收益,保证交易市场的长期稳定运行。

面向存算联调的跨云纠删码自适应数据访问方法

日益旺盛的跨云存算联调需求对跨云数据访问速度提出较高要求.因此,跨云数据访问速度较高的基于数据冗余技术(纠删码和多副本)的跨云数据访问方法逐渐受到关注.其中,基于纠删码的跨云数据访问方法因其存储开销较低、容错性较高而成为当前研究热点.为通过缩短编码块传输用时以提高数据访问速度,现有基于纠删码的跨云数据访问方法尝试引入缓存技术并优化编码数据访问方案.然而,由于现有方法的缓存管理粒度较粗且未协同优化缓存管理与编码数据访问方案,导致其存在缓存命中量低、缓存命中增效低、低传输速度编码块访问量大等问题,使得其编码块传输用时仍较长.为此,首先提出了一种基于星际文件系统(interplanetary file system,IPFS)的跨云存储系统框架(IPFS-based cross-cloud storage system framework,IBCS),可基于IPFS数据分片管理机制实现细粒度的缓存管理,从而可提高缓存命中量.然后,提出一种面向存算联调的跨云纠删码自适应数据访问方法(adaptive erasure-coded data access method for cross-cloud collaborative scheduling of storage and computation,AECAM).AECAM以编码块(含缓存编码块)与数据访问节点的分布为依据评估数据访问过程中各编码块的传输速度,并据此制定可避免访问低传输速度编码块的编码数据访问方案.此外,AECAM可识别出其制定编码数据访问方案时易选中且实际传输速度较低的编码块,并将其缓存在数据访问节点附近,从而可同时提高缓存命中量和命中增效.最后,基于IBCS和AECAM构建了面向跨云存算联调的存储系统(cross-cloud storage system for collaborative scheduling of storage and computation,C2S2).跨云环境下的实验表明,相较于现有引入缓存的基于纠删码的存储系统,C2S2可以将数据访问速度提高75.22%~81.29%.

云桌面技术在高校计算机机房管理中的应用研究

在当前数字化教育的背景下,高校计算机机房作为信息技术教学的核心场所,其管理和运营的高效性对教学质量有着直接影响。文章聚焦于云桌面技术在高校计算机机房管理中的应用,探讨该技术如何优化计算机机房的资源利用、提高管理效率及增强教学活动的灵活性和安全性,着重分析模板管理、桌面管理和教室管理的策略与实践,旨在实现资源的高效分配和利用。

云计算技术在计算机大数据分析中的运用

互联网和信息技术的快速发展,大量的数据被产生和积累,这些数据往往具有高维、高速和多样的特点,传统的数据处理技术已无法有效处理和分析这些海量数据,因此大数据分析技术应运而生。基于云计算技术的弹性计算能力、高效的储存和数据管理功能、实现分布式计算和并行处理,提出云计算技术在计算机大数据分析中的具体应用,分别是构建高效的数据处理模型、创建计算机网络分析模块、打造易于读取的数据储存平台、实现安全扫描和链路加密,进而保证大数据的收集、处理和分析都能更高效。

基于云计算和大数据技术的传感器数据存储与分析系统设计

针对当前传感器数量剧增,导致传感器测量数据复杂性高的特点,提出了基于云计算和大数据技术的传感器数据存储与分析系统,用于传感器数据的收集、存储和分析。该系统采用开源技术,提供端到端的传感器数据生命周期管理和分析工具。在Hadoop MapReduce框架的基础上,结合分布式机器学习框架中的k-means算法,构建一个可扩展的机器学习库。在弹性分布式数据集Spark上运行提出的系统,结果表明,该系统能够有效地对大量数据点进行聚类,与机器学习软件Weka相比,提出的系统可以分析数百万个坐标而不会出现性能问题,证明了其在大规模传感器数据分析应用中的有效性。

数据库技术在船舶破舱稳定性计算中的应用

船舶破舱稳定性的计算会随着船舶类型、舱位、人数、碰撞位置不同而不同,目前尚未建立一个统一的计算模型来实现船舶破舱稳定性计算,且破舱稳定性计算量大,建立的计算模型较为复杂。本文提出使用数据库技术、云平台技术建立一个综合的船舶破舱稳定性云计算平台,对概率性船舶破舱稳定性计算过程进行充分研究,计算分舱指数和概率因数,使用数据库技术实现对破舱稳定性计算相关数据的存储及查询,打通所有应用端、存储端和计算端的接口,并通过云平台的性能监控优化船舶破舱稳定性的计算模型。

云环境下大数据计算机处理技术应用研究

云环境中,通过大数据可以实现冗余信息的处理与分析,通过数据挖掘等技术手段的支持可以实现信息资源的优化与处理。为了充分满足云环境中数据处理的需求,通过现代技术手段综合分析,可以切实保障信息数据的安全性。大数据时代,计算机技术高度发展,可以实现多种信息数据与资源的整合,对于国家、企业以及人们的信息安全具有重要的影响。在云环境中,为了实现信息数据处理的安全性、高效性,要基于实际状况对信息数据的采集、存储以及安全管理等方面进行综合分析。

基于云计算虚拟化技术的智慧旅游信息服务平台设计

为提高旅游服务信息网络资源利用率和增加游客量,设计基于云计算虚拟化技术的智慧旅游信息服务平台。该平台通过具备各类服务器、网络设备以及接口端的物理层完成旅游资源调度、分配以及管理,实现旅游资源集成化,并通过相对应的分布式处理方式,将信息、旅游资源虚拟化。实验结果表明:该平台具有较好的大数据处理效率以及应用性,可降低旅游企业50%的运行成本,使游客量大幅度提升。

基于云计算的大数据安全存储及处理方案分析

阐述一种基于云计算的大数据安全存储和处理方案,该方案采用多层安全策略、加密技术和众包技术,具有高可扩展性和高效性,从而有效解决传统数据存储和处理的瓶颈问题。

基于云原生架构的企业级Elasticsearch平台研究

为解决Elasticsearch在企业级应用场景下,基础资源利用率与运维效率低、容器的数据持久化及资源需求多样性等方面问题,基于Ceph和Kubernetes构建了云原生的企业级Elasticsearch平台。对Elasticsearch平台的架构高可用性、数据持久化、容器化部署及资源标准化交付进行关键设计,对架构的优势进行定性分析,并与传统部署架构进行性能方面的定量对比测试。实验结果表明,该平台在架构设计方面优势明显,在性能方面表现良好,在企业级应用中具有较好的运维与经济效益。

基于虚拟分布式技术的网络数据采集系统设计与分析

网络数据采集是网络系统研制过程中重要的一环,能够为系统排故分析提供可靠的数据支撑,保障系统安全稳定运行。为提高数据采集系统对复杂网络的采集效率,文章设计一款基于虚拟分布式技术的网络数据采集系统,并围绕系统实现、可靠性与实时性分析等方面展开论述,通过对系统架构的改进,计算机硬件资源得以充分利用,提高了数据采集的效率,可为系统试验分析提供可靠支撑。

基于云边自动化的工业控制系统关键技术及前景展望

本文探讨了基于云边自动化的工业控制系统的关键技术和未来发展前景。随着网络技术和人工智能的发展,工业控制系统正在从传统的分层、封闭架构向开放、平台化架构转变。云边自动化结合了云计算和边缘计算,优化了通信效率、系统灵活性和资源利用率。本文还讨论了虚拟化技术、网算控互动机制、新一代通信技术等方面的挑战和解决方案。最后,本文展望了云边自动化在设计制造一体化、IT/OT融合等领域的应用前景。