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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. [ABSTRACT FROM PUBLISHER]

With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and virtual machine (VM ) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multi-criteria-based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius’ applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA. [ABSTRACT FROM PUBLISHER]