Analysis of QoS aware energy-efficient resource provisioning techniques in cloud computing

By incorporating on-demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay-per-use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment-friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy-efficient cloud is the main objective of this paper. The paper will comprehensively review several energy-efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems.     

基于云网边端协同计算的智能分发网络研究

智能服务机器人将成为社会发展的重要组成部分和人类工作与生活的重要助手。通过简要介绍由人工智能的云端大脑、基于5G构建的安全神经网络和多关节的机器人本体所组成的云端机器人,提出了基于云、网、边、端协同计算的智能分发网络(Intelligence Distribution Network,IDN)的概念,将IDN应用于云端机器人,以提升云端机器人的智能程度。详细阐述了IDN的架构,从算力、算法、通信、数据、安全等角度对IDN进行了研究,对比分析了智能分发网络与内容分发网络(Content Delivery Network,CDN)的异同点;IDN之于人工智能类似于CDN之于互联网内容,对IDN的典型应用场景进行了测试分析。通过使用IDN,提升了云端机器人智能程度和响应速度、降低了上行带宽和服务成本。尽管当前处于云端机器人的发展初期,但IDN将是实现云端智能机器人大规模商用的必经之路和关键技术,IDN将成为继CDN产业之后的又一个新领域。     

基于云计算的工业互联网数据资源管理平台构建

云计算作为一种新兴的商业计算模型,融合了分布式计算、并行计算、效用计算等计算机技术与网络技术的优势,为互联网络提供了一个安全、可靠的运行环境。为推进工业互联网平台的自动化、智能化、数字化、网络化发展,技术人员将云计算技术与工业互联网数据资源管理平台的构建工作融为一体,旨在发挥云计算模式低成本、高效率与高可用性的应用优势,以增强工业互联网平台的安全性、可靠性与稳定性。基于此,文中着重围绕基于云计算的工业互联网数据资源管理平台的构建思路展开了论述,以此来强调云计算技术在平台构建过程中的重要作用。     

面向移动互联网发展的3G Teleco 2.0

面向移动互联网,电信运营商的运营模式必须向Teleco2.0演进。IP多媒体子系统（IMS）将主要用于为企业大客户服务,而面向消费大众的娱乐化网络新媒体业务将基于智能结点重叠网/分布式业务网络（INON/DSN）。核心网将向扁平化演进,从I-HSPA到系统结构演进（SAE）,甚至进一步直接用互联网做核心网。客户端加门户模式配合云计算可以使廉价手机访问基于超文本传输协议（HTTP）的互联网。包月是用户唯一能够接受模式,廉价合理的价格将促进移动互联网大发展。     

基于云服务的电视实现方法研究

该文所述基于云服务的电视实现方法,是基于云计算的一种新颖的电视接人、视频内容分发的方法。此方法有别于目前基于互联网传送的各类智能电视,而是针对互联网云计算的商业模式,彻底解决互联网电视操作复杂、成本高的问题。该文提出的是一种不内置嵌入式计算机平台、不内置web浏览器,以及用户任务推送器,仅基于云服务的用户接入管理服务器来接人云端,采用虚拟显示技术的新颖云电视机技术,从而完成电视机与媒体资源之间的传递。     

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.     

基于云计算的智慧校园规划设计

随着物联网、云计算、大数据等技术的不断完善,智慧校园已经成为了多媒体校园建设的新模式。本文基于多媒体校园建设的现状,提出了一种基于云计算的智慧校园规划设计,为师生提供了一个智能化综合信息服务平台,可以实现对校园的可视化管理,是师生可以全面感知校园资源,提高资源的利用率,缩小校际距离。     

互联网服务的融合化、社会化与智能化

互联网在与社会的互动中不断创新,移动互联网、云计算、微博、网络视频等是互联网与电信、IT、媒体、娱乐相互融合形成的典型应用。融合化、社会化与智能化为互联网发展的主要趋势。     

云计算中数据隐私保护研究进展

由于社会分工和资源共享的必然,公共云平台必将成为和电网、互联网等同等重要的国家基础设施。云计算面临的安全问题制约着云计算的广泛使用。数据安全在云计算中尤为重要,如何保证数据的安全性是云计算安全的核心。从数据的隐私保护计算、数据处理结果的完整性认证、数据访问权限控制以及数据的物理安全4个方面对已有研究工作进行了分类和总结,为后续云计算中数据的安全性研究提供参照。     

移动互联网中的云计算安全架构

移动互联网(Mobile Internet)是以移动设备为核心,融合了互联网和移动通信技术的一种计算机技术,是当今信息科技发展的热点之一。而云计算(Cloud Computing)作为一种高效、灵活、节能的计算模式,是当前信息化建设的重要技术手段之一。移动互联网与云计算的结合,可以为用户提供更多、更丰富的服务,提高信息处理的效率和质量。然而,移动互联网中的云计算也面临着安全问题,如何构建安全、可靠的云计算安全架构,成了一个亟待解决的问题。