Effective Web-service Composition in Diverse and Large-scale Service Networks
Open Access
- Author:
- Oh, Seog-Chan
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 03, 2006
- Committee Members:
- Soundar Rajan Tirupatikumara, Committee Chair/Co-Chair
Dongwon Lee, Committee Member
Tom Michael Cavalier, Committee Member
Timothy William Simpson, Committee Member
Ling Rothrock, Committee Member - Keywords:
- web-service composition
AI planning
complex network
benchmark - Abstract:
- Web services are considered to be a potential silver bullet for the envisioned Service Oriented Architecture, in which loosely coupled software components are published, located and executed as parts of distributed applications. Web services intend to take the public web and today's distributed systems to unexplored efficiencies while suggesting flexible interfaces for promoting a wide spectrum of activities in tomorrow's service networks. The main research focus of web services is to achieve interoperability between distributed and heterogeneous applications. Therefore, flexible composition of web services to fulfill the requirements of tasks is one of the most important objectives in this research field. Applications including B2B E-commerce and E-government as well as in the public web, are expected to benefit greatly from web service composition. Until now, service composition has been an impromptu, tedious, and fallible process involving continuous low-level programming. Furthermore, as the number of available web services increases, finding the right web services to fulfill the given goal becomes intractable. In this dissertation, we propose an AI planning-based framework for the automatic composition of web services. For this purpose, we explore the following issues. First, we formulate the web-service composition problem in terms of AI planning and network optimization problems to investigate its complexity. Second, we analyze publicly available web service sets using complex network analysis techniques. Third, we develop a novel web-service benchmark called WSBen. Fourth, we develop a novel AI planning-based heuristic web-service composition algorithm named WSPR. Finally, we conduct extensive experiments to verify WSPR against state-of-the-art AI planners. It is our hope that WSPR and WSBen will provide useful insights for researchers to develop web-service discovery and composition algorithms, and software.