Towards minimizing cost for composite data-intensive services

Service-oriented architecture provides a scalable and flexible framework to implement loosely-coupled, standards-based, and protocol-independent distribute computing. One of its goals is to make use of the distributed services with different functions to build powerful composite services. Service composition is an active research area in service computing. Existing research is endeavoring to achieve desirable quality levels of composite services and improve customer satisfaction. The service-oriented approach using Web services is also of great interest for the implementation of data-intensive processes such as data mining, image processing and so on. The applications based on data-intensive services have become the most challenging type of applications in service-oriented architecture. The service-based strategy provides maximal flexibility when designing data-intensive applications. Huge data sets that may each be replicated in different data centers have to be exchanged between several services. The movement of mass data influences the performance of the whole application process. Especially, the price of services will be different when considering the data center´s locations and the amount of data transferred. It is desirable to find the cost minimized service composition solution in service computing. Therefore, how to select appropriate data centers for accessing data replicas and how to select services with lowest associated costs are emerging problems when deploying and executing data-intensive service applications. In this paper, a cost minimizing service composition model for data-intensive applications is proposed. Furthermore, how bio-inspired algorithms offer advantages to solve such problems will be presented.