User continuance intention to use cloud storage service

Cloud computing is a huge and important change in the field of network application in recent years to provide users with a completely different IT service and delivery mode. Among various cloud services, cloud storage is a service most closely related to web users’ need because it involves the storage of users’ all important data and backup files. In this study, a sample survey was conducted in Taiwan, and key factors influencing individual users’ adoption of the cloud storage service were analyzed and discussed based on Task-Technology Fit theory. The research results indicate that “cloud storage service”, “unstructured task”, “cloud storage self-efficacy” and “opinion of reference groups” all have significant positive influences on the “perceived usefulness”, which further has influence on users’ continuance intention to use the cloud storage service. The findings also support that the privacy protection risk and the lack of privacy-policy risk in the cloud storage service produce negative moderating effects on the perceived usefulness and the continuance intention.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

基于云计算的图书馆云服务模型研究

通过借鉴OCLC在云计算图书馆的设计思路以及基于云计算整合图书馆资源与日常服务的办法,提出了在云计算图书馆云服务的构建思路及其模型。     

Highly reliable architecture using the 80/20 rule in cloud computing datacenters

Cloud datacenters host hundreds of thousands of physical servers that offer computing resources for executing customer jobs. While the failures of these physical machines are considered normal rather than exceptional, in large-scale distributed systems and cloud datacenters evaluation of availability in a datacenter is essential for both cloud providers and customers. Although providing a highly available and reliable computing infrastructure is essential to maintaining customer confidence, cloud providers desire to have highly utilized datacenters to increase the profit level of delivered services. Cloud computing architectural solutions should thus take into consideration both high availability for customers and highly utilized resources to make delivering services more profitable for cloud providers. This paper presents a highly reliable cloud architecture by leveraging the 80/20 rule. This architecture uses the 80/20 rule (80% of cluster failures come from 20% of physical machines) to identify failure-prone physical machines by dividing each cluster into reliable and risky sub-clusters. Furthermore, customer jobs are divided into latency-sensitive and latency-insensitive types. The results showed that only about 1% of all requested jobs are extreme latency-sensitive and require availability of 99.999%. By offering services to revenue-generating jobs, which are less than 50% of all requested jobs, within the reliable subcluster of physical machines, cloud providers can make their businesses more profitable by preventing service level agreement violation penalties and improving their reputations.     

Resource virtualization and service selection in cloud logistics

Cloud computing and Internet of Things have promoted a new logistics service mode, i.e., the cloud logistics mode. This work studies the resource virtualization and service encapsulation of a logistics center, and focuses on the technologies of resource expression and service encapsulation. After the resources of a logistics center are encapsulated in web services, how to find the “best” concrete web service among many is a critically important issue. This work considers service selection as an optimization problem and establishes a Particle Swarm Optimization (PSO)-based web service selection model with quality of service (QoS) constraints. It can be used to address the horizontal adaptation issues from the composite web services. The feasibility and effectiveness of the model are verified by several experiments.     

DaaS: Cloud-based mobile Web service discovery

The proliferation of smartphones and the recent advancement in ubiquitous wireless access have made mobile Web services more possible than ever before. However, finding relevant Web services that can match requests and fit user context remains a major concern. The challenges facing Web service discovery are further magnified by the stringent constraints of mobile devices and the inherit complexity of wireless heterogeneous networks. Cloud computing, with its flexible design and theoretically unlimited computing resources, is a viable approach to bootstrapping Web service discovery. The cloud can build bridges between mobile devices, as a convenient ubiquitous interface, and a backbone infrastructure with abundant computing resources. This paper introduces “Discovery as a service (Daas)”, a novel cloud-based discovery framework that addresses the core components of mobile Web service discovery. The DaaS framework lays the foundation of efficient mobile Web service discovery that takes into consideration user preferences and context. The experimental validation and performance evaluation demonstrate that DaaS can effectively rank relevant services according to the various user context and preferences, in addition to enhancing the precision of the discovered services. The prototype also shows that Web service clustering for discovery significantly improves the overall response time, while the cloud maintains scalability according to prespecified performance criteria.     

A method for trust management in cloud computing: Data coloring by cloud watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtualized resources are provided as services. With virtualization technology, cloud computing offers diverse services (such as virtual computing, virtual storage, virtual bandwidth, etc.) for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.     

Secure design for cloud control system against distributed denial of service attack

Nowadays, the development of cloud computing has given power to the resource constrained network control system (NCS) to out source heavy computations to the cloud server. However, the development of Cloud Computing produced many security challenges regarding the cyber physical connection between the cloud and control system. The connection between the control system and cloud server can be subjected to distributed denial of service (DDoS) attack by an attacker to destabilize the NCS. In this paper, we will address this issue by building a secure mechanism for such systems. We will design a detection approach and a mitigation approach for better stable performance of NCS. To ensure the stability of NCS at the time of DDoS attack, we will also design a switching mechanism (SM) for cloud control system (CCS) when there are no more real time solutions available from the cloud. Finally, we will apply the proposed mechanism to an unmanned arial vehicle (UAV). Our simulation results show that the mechanism works well in stability and protection of NCS under DDoS attack.     

浅析云计算及其应用

云计算是一种创新的商业计算服务,云计算是将计算任务分配到云网平台中,为各种应用系统提供计算、存储以及各类软件服务。本文首先概述了云计算的定义和构成,并介绍了云计算的特点,最后探讨了云计算的应用问题。     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.     

A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters

Information and communication technology (ICT) has a profound impact on environment because of its large amount of CO₂ emissions. In the past years, the research field of “green” and low power consumption networking infrastructures is of great importance for both service/network providers and equipment manufacturers. An emerging technology called Cloud computing can increase the utilization and efficiency of hardware equipment. The job scheduler is needed by a cloud datacenter to arrange resources for executing jobs. In this paper, we propose a scheduling algorithm for the cloud datacenter with a dynamic voltage frequency scaling technique. Our scheduling algorithm can efficiently increase resource utilization; hence, it can decrease the energy consumption for executing jobs. Experimental results show that our scheme can reduce more energy consumption than other schemes do. The performance of executing jobs is not sacrificed in our scheme. We provide a green energy-efficient scheduling algorithm using the DVFS technique for Cloud computing datacenters.     

Exploring vendor capabilities in the cloud environment: A case study of Alibaba Cloud Computing

Cloud computing is being viewed as an effective enabler for enhanced operational efficiency and flexibility in competitive business environments. Despite the increased knowledge on cloud computing, very few studies have been conducted to explore vendors' capabilities for service development and delivery. This gap prevents us from developing a full understanding of the service provision process and the actions through which vendors develop cloud services and create value for clients. In this paper, we present an in-depth qualitative case study of Alibaba Cloud Computing, China’s biggest Infrastructure-as-a-Service (IaaS) vendor, and identify five important capabilities, namely, cloud platform development, cloud platform deployment, IaaS imitation, IaaS commercialization, and IaaS improvement. We suggest that these capabilities and the associated actions are central to vendors’ adoption of cloud computing, acquisition of knowledge, and delivery and improvement of their cloud service provision. This paper contributes incrementally to the evolving scholarship on cloud computing and also offers useful guidelines for current and aspiring vendors.     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

云计算数据服务主动安全防御系统探讨

随着云计算数据服务的快速发展和应用,大大地改变了人们的工作、学习和生活模式。云计算数据服务带来极大便利的同时,也给人们带来了潜在的威胁,因此需要构建网络安全主动防御系统,提高网络安全性能。文章详细地分析了云计算数据服务面临的安全威胁,阐述了云计算服务平台主动安全防御系统及采用的技术,提高了云计算数据服务平台的安全性能。     

利用可信计算技术改善云计算的安全性

云计算影响了互联网上世界上任何地方远程服务器处理、数据存储和共享的方式。这种共享多种分布式资源方式,使得安全问题更加复杂化。本文分析了云计算环境下的安全服务,通过整合可信计算环境来建立云计算系统。可信计算平台模式可以提高云计算的安全性。可信计算模式重要的安全服务包括加密,认证,完整性和保密性等都可以用在云计算系统中。     

Performance analysis of cloud computing services considering resources sharing among virtual machines

Cloud computing is a novel paradigm for the provision of service on demand. Through the use of virtualization, physical resources are converted into “resources pool” which provides service on demand. Due to the complexity and diversity of user requests of the cloud computing system, its performance is difficult to model and analyze. Although there are some researches on cloud service performance, very few of them addressed the issues of resources sharing among VMs and its impact on service performance. This paper presents a model for performance analysis of cloud services. The model considers the resources sharing among VMs. In addition, various types of failures, such as VMs failures, physical servers failures and network failures are also considered. The service requests are also relaxed compared with prior research. A service request is divided into many subtasks and each subtask consists of a series of data processing and transmission. The average service time of service requests is obtained. And a numerical example is presented.     

企业云服务体系结构的参考模型与开发方法

服务与云计算范型的融合有助于大规模分布式软件的开发和应用,同时也为面向服务的软件工程带来了新的挑战。云计算的最大挑战是缺少事实上的标准或单一的体系结构方法,以满足企业将关键产品作为Internet上的云服务发布的应用需求。首先,针对企业云计算的业务特点,提出了一种企业云服务体系结构(Enterprise Cloud Service Architecture,ECSA)风格的通用和抽象参考模型,分析了该模型中的云服务、服务模式、服务消费者、管理、流程、质量属性、服务构件模型、服务匹配和交互模式匹配9个组件及其之间的关系,并讨论了它们中的角色。然后,提出了一个四阶段的ECSA迭代改进过程,该过程把云服务视为首要的类建模元素,通过解除云服务模型和来自目标构件配置之间的耦合,可实现相同云服务集的多种不同体系结构。最后,给出了一种基于该模型的期货程序化交易的私有云服务应用实例,用以展示该方法的可行性和有效性。     

基于云服务的城市级智能视频监控系统架构初探

云服务计算已经成为未来发展的一种IT服务模型．它不仅可以实现视频资源的完全虚拟化并简化和加速访问过程,大大节省客户建设成本,同时提供更强的计算和共享功能。主要介绍用于城市社会治安和城市管理职能的视频监控系统的云服务模型及架构。     

Cloud services selection: A systematic review and future research directions

Cloud computing has developed in popularity as a large-scale computing paradigm that offers a range of computing resources as a service through the internet on a pay-as-you-go basis. The expansion in demand and commercial availability of cloud services brings new challenges to cloud services selection. Several research studies have been conducted to develop enhanced methodologies to assist service consumers in selecting appropriate services. In this paper, 105 primary studies published during January, 2011 to May, 2022 has been selected using a multi-stage scrutinizing approach. The selected preliminary studies were further classified based on various variables to answer the research questions stated for this work. A systematic review of existing cloud service selection approaches is performed, which are analyzed along eight dimensions: decision-making methods, context, purposes, cloud service performance parameters, simulation/language tools, domain, datasets, and experiment/validation methods. After a thorough review and comparison of these approaches across the above-mentioned dimensions, several open research issues in the current literature have been identified. The contribution of this research is fourfold: focusing on state-of-the-art cloud services selection approaches, highlighting the benefits and drawbacks of various cloud services selection methodologies and their future directions, offering a taxonomy based on a thorough literature study, and identifying nine critical challenges in cloud services selection that require further investigation. This systematic review study is anticipated to benefit both academics and business experts.