The increasing number of Software-as-a-Service(SaaS) services available in the cloud market make them plausible and attractive for building cloud-based applications. However, performance instability is common in the cloud environment due to changes in supply and demand of shared computational infrastructure and resources. Candidate services are vulnerable to such instability. Current service selection and composition approaches do not explicitly address performance fluctuations when building cloud-based applications. This thesis proposes a novel approach to improve performance stability by leveraging on the principles of design diversity and portfolio-based thinking when selecting and composing cloud-based applications. The objective is to minimize the risks that could stem from selecting and composing cloud-based services that are vulnerable to performance instability.
In this thesis, we use two scenarios to illustrate the applicability and the effectiveness of the approach. As scalability is of paramount importance for efficient dynamic and adaptive selection and composition, the thesis adapt a systematic method to identify the various scalability dimensions that can affect the working of the approach and consequently evaluate the sensitivity of the approach to the identified dimensions. The thesis concludes with possible directions for future work
