User feature-aware trustworthiness measurement of cloud services via evidence synthesis for potential users

Cloud computing can provide elastic and dynamic resources on demand, which facilitates service providers to make profits resulting from the long tail effect. It becomes vitally important to ensure that cloud services can be acceptable to more potential users. However, it is challenging for potential users to discover the trustworthy cloud services due to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. This paper presents a user feature-aware trustworthiness measurement approach for potential users. In this approach, the influence factors of QoE are systematically analyzed based on the user feature model and the quantitative computation methods are designed to measure the user feature similarity. In addition, employing FAHP (fuzzy analytic hierarchy process) method identifies the user feature community. To enhance the accuracy of trustworthiness measurement, the false evidences in QoE evaluations are iteratively filtered out with dynamic mean distance threshold. Finally, the trustworthiness of service is measured via evidence synthesis combining user feature similarity. The experiments show that this approach is effective to improve the quality of trustworthiness measurement, which is helpful to solve information overload problem and cold start problem of trusted service recommendation for potential users.     

Multi-valued collaborative QoS prediction for cloud service via time series analysis

Aiming at the diversity of user features, the uncertainty and the variation characteristics of quality of service (QoS), by exploiting the continuous monitoring data of cloud services, this paper proposes a multi-valued collaborative approach to predict the unknown QoS values via time series analysis for potential users. In this approach, the multi-valued QoS evaluations consisting of single-value data and time series data from consumers are transformed into cloud models, and the differences between potential users and other consumers in every period are measured based on these cloud models. Against the deficiency of existing methods of similarity measurement between cloud models, this paper presents a new vector comparison method combining the orientation similarity and dimension similarity to improve the precision of similarity calculation. The fuzzy analytic hierarchy process method is used to help potential users determine the objective weight of every period, and the neighboring users are selected for the potential user according to their comprehensive similarities of QoS evaluations in multiple periods. By incorporating the multi-valued QoS evaluations with the objective weights among multiple periods, the predicted results can remain consistent with the periodic variations of QoS. Finally, the experiments based on a real-world dataset demonstrate that this approach can provide high accuracy of collaborative QoS prediction for multi-valued evaluations in the cloud computing paradigm.     

面向个性化需求的云制造服务可信评价模型

针对传统的云制造服务可信评价模型中存在的可扩展性弱、难以满足个性化需求等问题,提出一种可扩展性强、可以较好地满足个性化需求的可信评价模型.首先构建多层次的、多粒度的云制造服务可信评价框架;然后基于此框架,提出了基于云模型的云制造服务可信评价方法,在该方法中,引入云模型理论,用于统一表征不同类型的评价指标,以及描述用户的...     

Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern

Retaining users and facilitating continuance usage are crucial to the success of mobile social network services (SNS). This research examines the continuance usage of mobile SNS in China by integrating both the perspectives of social influence and privacy concern. Social influence includes three processes: compliance, identification and internalization, which are respectively represented by subjective norm, social identity, and group norm. The results indicate that these three factors and privacy concern have significant effects on continuance usage. The results suggest that service providers should address the issues of social influence and privacy concern to encourage mobile SNS continuance usage.     

Strategic signaling through cloud service certifications: Comparing the relative importance of certifications’ assurances to companies and consumers

Cloud service certifications (CSCs) are assessed by practitioners to support strategic cloud adoption decisions with the aim to reduce information asymmetries. Both businesses and consumers scrutinize CSCs’ assurances as ex ante signals indicating a cloud provider’s future service quality. While some research has examined the aggregate effects of certifications on decision variables, recipients’ evaluations of certifications and their assurances before making IT-related decisions have received little attention. Furthermore, prior research has predominantly focused on privacy and security assurances in e-commerce certifications. Drawing on signaling theory, we propose that certifications are signals that recipients decompose into a set of fine-grained assurance signals that they weigh to evaluate certifications. We evaluate the responses of 113 company representatives and 317 consumers to a best-worst scaling survey to examine the relative importance these two groups attach to ten assurances from CSCs. Our results show that similar to other online contexts, security and privacy are important assurances, but additional assurances related to availability, the customer friendliness of contracts, and legal compliance are also demanded, particularly by companies. Privacy, security, and availability are most crucial to both companies and consumers, but their relative importance varies substantially between the two groups. Post-hoc subgroup analyses reveal significant differences in assurances’ relative importance for provider and user companies, adopter and non-adopter consumers as well as companies using different types of services and from different industries. Our findings indicate that recipients evaluate certifications as a bundle of signals with varying importance due to recipients’ characteristics and context. With this conceptualization, we contribute to an advanced understanding of the sense-making of certifications and lay out how it influences cloud service adoption theories. Our study has practical implications for certification authorities that design CSCs as well as for providing insights to cloud service providers on customers who draw on CSC assurances when making cloud service adoption decisions.     

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.     

Recommend trustworthy services using interval numbers of four parameters via cloud model for potential users

How to discover the trustworthy services is a challenge for potential users because of the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. Aiming to the limitations of traditional interval numbers in measuring the trustworthiness of service, this paper proposed a novel service recommendation approach using the interval numbers of four parameters (INF) for potential users. In this approach, a trustworthiness cloud model was established to identify the eigenvalue of INF via backward cloud generator, and a new formula of INF possibility degree based on geometrical analysis was presented to ensure the high calculation precision. In order to select the highly valuable QoE evaluations, the similarity of client-side feature between potential user and consumers was calculated, and the multi-attributes trustworthiness values were aggregated into INF by the fuzzy analytic hierarchy process method. On the basis of ranking INF, the sort values of trustworthiness of candidate services were obtained, and the trustworthy services were chosen to recommend to potential user. The experiments based on a realworld dataset showed that it can improve the recommendation accuracy of trustworthy services compared to other approaches, which contributes to solving cold start and information overload problem in service recommendation.     

eScience in the Social Cloud

Social networks offer great potential for fostering collaboration between individuals and amongst groups. This potential collaborative environment is not only applicable for recreation, but can also provide considerable value to diverse research communities. For this reason scientists are increasingly utilizing social networking concepts in projects to form groups, share information, publicize their work and communicate with their peers. This article describes two different approaches to supporting eScience, by providing scientific computing and collaboration within what we term the Social Cloud. In our first approach the social network is used as a collaborative overlay, in combination with the ad hoc creation of infrastructure composed of virtual machine clusters built from resources contributed, by the users, to the Social Cloud. Our second approach is based around the principle of volunteer computing, where the Social Cloud provides researchers with a platform to exploit social networks by reaching out to non technical users who would otherwise be unlikely to donate computational time for scientific and other research. In this article we specifically explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through the use of incentives in existing social networks.     

一种SOA云服务平台架构研究与应用

在完美SOA分布式系统架构平台基础上,面向服务进行云架构模式的分析,建立以云端用户、云服务平台中心和云服务提供者构成的云架构模式,实现SOA云平台体系架构,将云服务提供者分成云服务接口层、云服务调度管理层、云计算服务层和云物理服务层。经过改进提高了云平台的服务能力和执行效率,并给出了提高该云平台调度效率的调度算法。通过数字校园云进行了云平台实例演示,显示了SOA云服务平台的架构和调度算法的快速服务响应能力。     

云计算研究现状综述

云计算能够给用户提供可靠的、自定义的、最大化资源利用的服务,是一种崭新的分布式计算模式。同时,云计算和其他技术及理论的有机结合,也是解决理论研究和实际应用的重要途径。阐述了云计算的基本概念,论述了云计算的优势和存在的问题,比较了云计算不同于以往技术的新特点,剖析了云计算的框架,介绍了不同的云计算服务,并从云计算服务类型和框架层次的角度进行了综述,分析了二者之间的对应关系,概括了围绕云计算框架和不同服务所展开的相关研究工作。结合已有的云计算研究成果,展望了未来的研究方向和亚待解决的关键问题。     

Keeping the user in the cloud: a cognitive social capital antecedent to use continuance and trust-commitment in personal cloud storage services

While personal cloud storage services are popular, providers experience difficulty retaining users. Two explanations of user continuance intention, being use continuance theory and trust-commitment theory, emerge from prior literature. However, there is no unifying theoretical lens to reconcile these two explanations. We use a cognitive social capital explanation to unify these two literature streams. We apply our research model to 237 cloud storage users. We find that the cognitive social capital lens significantly predicts both use continuance intention and trust-commitment in personal cloud storage services. Implications for cloud service providers are offered.     

Cooperative private searching in clouds

With the increasing popularity of cloud computing, there is increased motivation to outsource data services to the cloud to save money. An important problem in such an environment is to protect user privacy while querying data from the cloud. To address this problem, researchers have proposed several techniques. However, existing techniques incur heavy computational and bandwidth related costs, which will be unacceptable to users. In this paper, we propose a cooperative private searching (COPS) protocol that provides the same privacy protections as prior protocols, but with much lower overhead. Our protocol allows multiple users to combine their queries to reduce the querying cost while protecting their privacy. Extensive evaluations have been conducted on both analytical models and on a real cloud environment to examine the effectiveness of our protocol. Our simulation results show that the proposed protocol reduces computational costs by 80% and bandwidth cost by 37%, even when only five users query data.     

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.     

Understanding Service Quality and System Quality Success Factors in Cloud Archiving From an End-User Perspective

This study seeks to explain the adoption of cloud storage services as a means of personal archiving thereby focusing on users’ service and system quality perceptions and their drivers. The authors derive and empirically validate a model that incorporates users’ perceptions of service/system quality as well as behavioral factors to explain usage. Finally, the authors highlight important determinants of system/service quality perceptions that cloud providers should pay attention to in their attempts to increase marketshare.     

Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits

Mobile payment is an emerging and important application of mobile commerce. The adoption and use of mobile payment services are critical for both service providers and investors to profit from such an innovation. The present study attempts to identify the determinants of pre-adoption of mobile payment services and explore the temporal evolution of these determinants across the pre-adoption and post-adoption stages from a holistic perspective including behavioral beliefs, social influences, and personal traits. A research model that reflects the characteristics and usage contexts of mobile payment services is developed and empirically tested by using structural equation modeling on datasets consisting of 483 potential adopters and 156 current users of a mobile payment service in China. Our findings show that behavioral beliefs in combination with social influences and personal traits are all important determinants for mobile payment services adoption and use, but their impacts on behavioral intention do vary across in different stages. Theoretical and practical implications of the findings are presented.     

基于TAM的企业云应用服务采纳模型及关键因素

云计算将主导企业应用的未来已成为业界的共识,转向云计算是企业应用模式的颠覆性变化。企业用户需要采纳适合企业自身需求的云计算方案,具备评估采纳云应用服务风险的能力;而服务提供商则应提供满足不同企业需求的云应用服务。采用技术接受模型(TAM)的相关理论建立云应用服务技术接受模型,并运用模糊决策实验与实验评估法(DEMATEL)对所设计模型及实例数据进行分析,检验所设计的云应用服务技术接受模型及关键因素。研究结果表明,感知有用性、应用服务质量、社交影响对企业用户采纳产生关键影响。     

Robo-advisors and investment intention: A perspective of value-based adoption

Financial robo-advisors have been widely used to assist individuals in their investment decisions, making it important to reduce uncertainties in the assistance process. Existing empirical studies rarely explore uncertainty reduction strategies and their implications on users’ investment intentions in the context of financial robo-advisors; our study attempts to address this gap. We construct a model to explain how uncertainty reduction strategies affect users’ investment intention in using financial robo-advisors. By collecting and analyzing a sample of 307 financial robo-advisor users, we find that algorithmic interpretability, structural assurance, and interactivity as uncertainty reduction strategies are positively related to users’ investment intention through the value-based adoption mechanism. Our research extends the value-based adoption model and uncertainty reduction theory in the financial robo-advisor context. We provide insights to financial robo-advisor service providers about focusing on improving algorithmic transparency, third-party assurance, and interactivity of financial robo-advisors to enhance perceived value and investment intention.     

Chargeback for cloud services

With pay-per-use pricing models, elastic scaling of resources, and the use of shared virtualized infrastructures, cloud computing offers more efficient use of capital and agility. To leverage the advantages of cloud computing, organizations have to introduce cloud-specific chargeback practices. Organizations have to allocate IT service costs to business users in a way that reflects service consumption. To help organizations become effective users of cloud services, this article provides an overview of the factors that influence chargeback in the cloud services. This is an initial work that determines the factors influencing the chargeback in the cloud services. The findings of this research facilitate organizations to realize the implications of the cloud for their chargeback.     

A resource elasticity framework for QoS-aware execution of cloud applications

Cloud infrastructures consisting of heterogeneous resources are increasingly being utilized for hosting large-scale distributed applications from diverse users with discrete needs. The multifarious cloud applications impose varied demands for computational resources along with multitude of performance implications. Successful hosting of cloud applications necessitates service providers to take into account the heterogeneity existing in the behavior of users, applications and system resources while respecting the user’s agreed Quality of Service (QoS) criteria. In this work, we propose a QoS-Aware Resource Elasticity (QRE) framework that allows service providers to make an assessment of the application behavior and develop mechanisms that enable dynamic scalability of cloud resources hosting the application components. Experimental results conducted on the Amazon EC2 cloud clearly demonstrate the effectiveness of our approach while complying with the agreed QoS attributes of users.     

基于HTTPS的云服务环境构建

在云计算高速发展的形势下,国内外越来越多的企业和个人将服务向云上迁移,从而实现敏捷、灵活管理,降低IT成本,以提高企业在新时代下发展动力和竞争力。本文提出基于安全超文本传输协议(HTTPS)的云服务环境的构建方法。通过云服务器的简单高效、安全可靠、处理能力可弹性伸缩等特性,并结合HTTPS的加密优势,从而达到为企业和个人不同的路径上提供不同的解决方案,并提供完备的解决方案,从而帮助企业和用户快速地搭建云服务环境。