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Service Mining for Trusted Service Composition in Cross-Cloud Environment

机译:跨云环境中可信服务组合的服务挖掘

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摘要

Nowadays, with the cloud's charismatic storage and computation power, more and more traditional services (social networking service, location-based services, etc.) are being migrated onto cloud platforms. These cloud services on different cloud platforms could be employed to form cross-cloud mobile applications of mobile cyber-physical systems (CPS). However, a cloud service may have various versions of quality of service (QoS) information revealed in different mobile CPS applications, which is often advertised as the elastic computation power. This characteristic makes it costly and time consuming to mine qualified ones from massive candidate cloud services for developing a mobile CPS application, as a service composition solution may have various evaluated values initiated by the various QoS properties. In view of this challenge, a cloud service selection method, named CSSM, is proposed in this paper. It takes the utility value as the evaluation index and aims at finding optimal or near-optimal trusted service composition solutions from a set of cloud services on users' demands. Technically, the user preference on each QoS metric is formalized as the preference interval for enhancing the fitness of a service composition solution. Furthermore, an extended top- k iteration composition process is performed among cloud services to get an optimal or near-optimal trusted service composition solution. Both theoretical analysis and experimental evaluation are conducted to guarantee the feasibility and efficiency of the CSSM.
机译:如今,凭借云的超凡魅力的存储和计算能力,越来越多的传统服务(社交网络服务,基于位置的服务等)正在迁移到云平台上。可以采用不同云平台上的这些云服务来形成移动网络物理系统(CPS)的跨云移动应用程序。但是,云服务可能具有在不同的移动CPS应用程序中显示的各种版本的服务质量(QoS)信息,这些信息通常被宣传为弹性计算能力。由于服务组合解决方案可能具有由各种QoS属性引发的各种评估值,因此该特性使得从大规模候选云服务中挖掘合格的云服务来开发移动CPS应用程序的成本高昂且费时。针对这一挑战,本文提出了一种云服务选择方法,称为CSSM。它以效用价值作为评估指标,旨在根据用户需求从一组云服务中找到最佳或接近最佳的可信服务组合解决方案。从技术上讲,将每个QoS指标上的用户偏好形式化为偏好间隔,以增强服务组合解决方案的适用性。此外,在云服务之间执行扩展的top-k迭代组合过程,以获得最佳或接近最佳的可信服务组合解决方案。为了保证CSSM的可行性和有效性,进行了理论分析和实验评估。

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