Cloud service selection using cloud service brokers: approaches and challenges

Cloud computing users are faced with a wide variety of services to choose from. Consequently, a number of cloud service brokers (CSBs) have emerged to help users in their service selection process. This paper reviews the recent approaches that have been introduced and used for cloud service brokerage and discusses their challenges accordingly. We propose a set of attributes for a CSB to be considered effective. DifFerent CSBs' approaches are classified as either single service or multiple service models. The CSBs are then assessed, analyzed, and compared with respect to the proposed set of attributes. Based on our studies, CSBs with multiple service models that support more of the proposed effective CSB attributes have wider application in cloud computing environments.     

面向绿色云计算的虚拟机迁移推荐

在以节约能源和提高能源利用率为目的的绿色云计算背景下,提出一种基于层次分析法的虚拟机迁移评估模型。该模型通过基于属性聚类的协同过滤方法,针对虚拟机的属性空间进行评分,给出了最佳绿色虚拟机迁移策略的决策方法。通过建立虚拟机任务资源使用率指标体系及两两比较矩阵,采纳五级评分制对虚拟机迁移及任务资源占用进行了评估。实验结果表明,提出的模型和方法能够显著提高云计算集群的能源利用率、节约能源的消耗。该方法对绿色云计算的研究具有重要意义。     

Privacy and e-commerce: a consumer-centric perspective

Privacy is an ancient concept, but its interpretation and application in the area of e-commerce are still new. It is increasingly widely accepted, however, that by giving precedence to consumer privacy bigger benefits can be reaped by all parties involved. There has been much investigation into the concept of privacy, legal frameworks for protecting this most impalpable of human values and, more recently, computing technologies that help preserve an individual’s privacy in today’s environment. In this paper we review the historical development of this fundamental concept, discussing how advancements both in society and in technology have challenged the right to privacy, and we survey the existing computing technologies that promote consumer privacy in e-commerce. Our study shows that historically the protection of privacy has been driven primarily both by our understanding of privacy and by the advancement of technology, analyses the limitations of privacy protections for current e-commerce applications, and identifies directions for the future development of successful privacy enhancing technologies.     

Multi criteria based personalized recommendation service using analytical hierarchy process for airbnb

The Journal of Supercomputing - There are many accommodation rental services like Hotels.com, Hotels Combined, Trivago, Airbnb, and so on. Airbnb, in particular, uses the service known as P2P (peer...     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

面向云应用的拟态云服务架构

针对单执行体的云应用服务缺乏异构性和动态性,难以应对未知漏洞和后门的安全威胁问题,提出一种拟态云服务架构,把云平台向用户提供的应用服务节点构造成基于拟态防御技术的服务包,使应用服务具有拟态构造带来的内生安全特性和鲁棒性,同时讨论了策略调度和裁决机制等两项关键的拟态云服务运行机制.经实验分析表明,拟态云服务具有较好的安全...     

流程挖掘在银行服务管理中的应用

随着银行服务信息化的不断发展,银行面临如何从大量的服务数据中提取有价值的信息用以提升服务效率的问题。在银行服务管理系统的实际应用中,由于其业务具有并发性事件多、日志数量大等特点,选择并行Apriori算法进行分析。与传统的Apriori算法相比,针对银行业务中并发性业务较多的特点,设计使用了并行Apriori算法,解决了单服务器运行效率随日志数量明显下降的弊端。银行服务管理系统每日会产生大量流程的日志数据,记录每一位参与员工的工作状态,通过调用并行Aporiori算法,挖掘服务流程日志中的关联规则,找出能够高效协作的员工组合。实验结果表明,将并行Apriori算法应用于服务流程日志的关联规则挖掘,使系统可以根据规则将协作关系紧密的员工分配在一起共同处理服务请求,提高了服务效率,取得了合理的应用效果,提高了银行服务管理系统中服务分配的智能。     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。     

Behavioral modeling based on cloud computing and target user recommendation for English cloud classroom

The English cloud classroom learning system provides a simple and effective way to access cloud-based training and learning materials. Student records based on behavioral modeling and interactive volume are assumed that the degree of student participation in the course measure. Previous studies, typically the frequency of student activities while ignoring temporary information. Analysis of Students' Activities in the course over time. According to the online classroom activity data, similar behavior change with time students are clustered. These results are three different groups. A large majority of inactive students. Another group to active throughout the students. However, a group of students to the event, but their activity will be reduced throughout the course. These student groups exhibit different performance. Overall students more active, the better the results. In addition to these general trends, also identified a trend of alternative courses that can be found, for example, becomes an active group of students in the course. It suggests that students' behavior is identified from a single course of conduct than the more complex, multi-mode learning activities course. More research is essential. In this study, investigated the Portuguese teachers 'views on classroom management, the relationship between self-off and the efficacy of teachers' and teachers' perception of classroom behavioral teachers' perception of time. Use mediator and moderator variables on the cloud classroom management, and teachers of English teachers perceive the relationship between classroom conditions were cheating process analysis.     

Deep knowledge-aware framework for web service recommendation

The Journal of Supercomputing - Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of...     

基于业务流程的制造云服务组合模型

为了提高云制造系统中制造云服务的组合成功率,实现组合云服务与用户业务需求的准确匹配,在对制造云服务、流程节点任务、云服务的可组合性和流程匹配进行形式化描述的基础上,提出一种基于业务流程的制造云服务组合模型。该模型由业务流程引擎、业务流程、选择逻辑、评估逻辑、监控逻辑、知识库和原子云服务集构成,在功能匹配的基础上,对候选服务的可组合性进行检查,结合负载、服务质量(QoS)和业务流程信息,选择合适的云服务,并将其挂接在业务流程上实现制造云服务的组合。对制造云服务的组合流程进行了详细描述,并给出云服务组合的实现方法。实例分析表明,该模型能够有效地选择满足业务需求的云服务实体并进行组合,从而提高制造云服务的组合成功率,保障用户制造活动的顺利进行。     

Toward a ubiquitous personalized daily-life activity recommendation service with contextual information: a services science perspective

In recent years, services science has emerged as a discipline of increasing importance, and one that aims to promote service innovation and increase service productivity by aligning disparate scientific, management, and engineering perspectives. It emphasizes that service innovation should be capable of creating value for both service providers and consumers. To realize the core thinking of services science, that is, attaining high value and high productivity, service design has to incorporate many factors into its consideration. Based on the ideas of this new research field, we develop a personalized daily-life activity recommendation service that includes: information behavior, business value, and technology architecture as our service design considerations. Our services can be requested in a ubiquitous environment and include users’ contextual information which is an important factor in information behavior. With regard to IT architecture, we use service-oriented architecture (SOA) that provides the flexibility and extensiveness of technology, as well as permits new innovative services to be easily added.     

Recurrent Tensor Factorization for time-aware service recommendation

With the advent of the era “everything is service ”, the emergence of Web services on the Internet is experiencing an exponential growth trend. How to recommend services to users that utilize sequential historical records has become one of the most challenging research topics in service computing. Tensor Factorization (TF) and Long Short Term Memory (LSTM) networks are two typical application paradigms for sequential service recommendation tasks. However, TF can only learn static short-term dependency patterns between users and services, ignoring the dynamic long-term dependency patterns between users and services. Although LSTM in Deep Leaning can learn dynamic long-term dependency patterns, it often encounters the trouble of vanishing gradient due to its complex gated mechanism. To address these critical challenges, we develop a novel model based on Deep Learning named Recurrent Tensor Factorization (RTF) with three innovations: (1) Three-dimensional interaction tensor of user–service-time was granulated into three fixed-size embedding dense vectors. (2) Personalized Gated Recurrent Unit (PGRU) and Generalized Tensor Factorization (GTF) simultaneously work on shared embedding dense vectors to memorize the long-term and short-term dependency patterns between users and services respectively. (3) Armed with GTF and PGRU, RTF is competent to predict the unknown Quality of Service (QoS) through comprehensive analysis. Experiments are conducted on real-world dataset, and the results indicate that our proposed method obviously outperforms six state-of-the-art time-aware service recommendation methods.     

Technologies for Web and cloud service interaction: a survey

The evolution of Web and service technologies has led to a wide landscape of standards and protocols for interaction between loosely coupled software components. Examples range from Web applications, mashups, apps, and mobile devices to enterprise-grade services. Cloud computing is the industrialization of service provision and delivery, where Web and enterprise services are converging on a technological level. The article discusses this technological landscape and, in particular, current trends with respect to cloud computing. The survey focuses on the communication aspect of interaction by reviewing languages, protocols, and architectures that drive today’s standards and software implementations applicable in clouds. Technological advances will affect both client side and service side. There is a trend toward multiplexing, multihoming, and encryption in upcoming transport mechanisms, especially for architectures, where a client simultaneously sends a large number of requests to some service. Furthermore, there are emerging client-to-client communication capabilities in Web clients that could establish a foundation for upcoming Web-based messaging architectures.     

Context-aware QoS prediction for web service recommendation and selection

QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.     

Discovering internal social relationship for influence-aware service recommendation

Existing approaches, such as semantic content-based or Collaborative Filtering-based recommendations, fail to exploit social aspects of services because services lack social relationships and do not consider social influence. In this paper, we propose a methodology for connecting distributed services in a global social service network (GSSN) to facilitate discovering internal social relationship for social influence-aware service recommendation. First, we propose a novel platform for constructing a GSSN by linking distributed services with social links based on quality of social link. We then propose a flexible model of the effective awareness of social influence, which provides a quantitative measure of the strength of influence between services. Next, a novel social influence-aware service recommendation approach is proposed based on GSSN using internal social relationship among services. The experimental results demonstrated that our new approach can solve the service recommendation problem with a low usage threshold and high accuracy, where the user preferences are exploited by a recommend-as-you-go method.     

Location-based deep factorization machine model for service recommendation

Applied Intelligence - The era of everything as a service led to an explosion of services with similar functionalities on the internet. Quickly obtaining a high-quality service has become a...     

面向不一致用户评价准则的在线服务推荐

客观上,用户的评价准则是由主观意识决定的,用户之间的评价准则不同导致多个用户对同一服务的评分不具备可比较性,不考虑不同用户评分的不可比较性所获得的服务推荐将难以满足用户个性偏好及其真实需求。为此,提出一种面向不一致用户评价准则的在线服务推荐方法,考虑用户偏好不一致时用户对在线服务的偏好关系,以偏好关系计算用户之间的相似度,并以此获得在线服务推荐结果。首先以用户-服务评分矩阵为基础建立用户对服务的偏好关系,其次根据偏好关系计算用户之间的相似度,然后以用户相似度为基础对用户未评分的服务进行评分预测,最后以预测评分的排序结果作为推荐结果。与经典的协同过滤推荐方法的比较实验,验证了本方法的有效性。实验表明,本方法获得的推荐结果能满足大多数用户的服务偏好,同时获得了比经典的协同过滤推荐方法更好的准确率。