Service Mining for Trusted Service Composition in Cross-Cloud Environment

Abstract

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.