Bio-inspired cost-aware optimization for data-intensive service provision

The rapid proliferation of enormous sources of digital data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio-inspired algorithms can overcome the new challenging requirements of data-intensive service provision. It is useful for the provision of data-intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. This thesis discusses the key concepts and approaches behind Web service concretization. It presents a hierarchical taxonomy of Web service concretization approaches and provides a detailed analysis of each approach. The taxonomy helps us not only to identify existing algorithms developed in service concretization but also to evaluate the applicability of different approaches to the data-intensive service provision problem. This thesis concentrates on the bio-inspired algorithms in the taxonomy. To this end, it conducts a systematic review of Web service concretization based on bio-inspired algorithms. The systematic review investigates the extent of applications of bio-inspired algorithms to QoS-based Web service concretization, which is not covered by previous studies. Then the comprehensive applications of an ant colony system and a genetic algorithm for cost-aware data-intensive service provision are introduced. The performance of the two proposed algorithms is evaluated and compared with other traditional methods. Further, this thesis investigates an ant colony system and a genetic algorithm for the multi-objective data-intensive service