Anytime QoS-aware service composition over the GraphPlan

Springer Science and Business Media LLC - Tập 9 - Trang 1-19 - 2013
Yuhong Yan1, Min Chen1
1Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada

Tóm tắt

Automatic service composition is the generation of a business process to fulfill business goals that cannot be fulfilled by individual services. Planning algorithms are frequently used to solve this problem. In addition to satisfying functional goals, recent research is geared toward selecting the best services to optimize the QoS of the result business process. Without considering QoS, the planning algorithm normally searches for the shortest plan, which actually implies the unit execution time for each service. With QoS, a longer plan may have better QoS values and thus is preferred over a shorter one. In this paper, we are motivated to combine a systematic search algorithm like Dijkstra’s algorithm with a planning algorithm, GraphPlan, to achieve both functional goals and QoS optimization at the same time. The planning graph generated by GraphPlan is a compact representation of all execution paths, which makes it feasible to apply Dijkstra’s principle. In our new QoSGraphPlan algorithm, we extend Dijkstra’s algorithm from working on a single-source graph to working on the planning graph whose nodes have multiple sources. Using our method, we can get the best QoS value for throughput and response time in polynomial time when they are the single criteria. For the other QoS criteria, such as execution time, reputation, successful execution rate, and availability, our algorithm is exponential for both single criterion problem and multiple criteria problem. In this case, we extend QoSGraphPlan with beam search to solve the combination explosion problem. As our algorithms search for an optimal solution during the process of constructing the planning graph, they belong to the category of anytime algorithms that return better solutions if they keep running for a longer time.

Tài liệu tham khảo

Bleul S (2009) Web service challenge rules. http://ws-challenge.georgetown.edu/wsc09/downloads/WSC2009Rules-1.1.pdf

Fredman ML, Tarjan RE (1987) Fibonacci heaps and their uses in improved optimalization problems. J ACM, 34

LaValle SM (2006) Planning algorithms, Cambridge

May Chan KS, Bishop J, Baresi L (2007) Survey and comparison of planning techniques for web service composition. Technical report Dept Computer Science, University of Pretoria

OASIS (2007) Uddi version 2.04 api specification. http://uddi.org/pubs/ProgrammersAPI-V2.04-Published-20020719.htm

OASIS (2007) Web services business process execution language (ws-bpel). http://www.oasis-open.org/committees/tc_home.php?wg_abbrev=wsbpel

Triantaphyllou E (2000) Multi-criteria decision making: a comparative study

W3C (2004) Owl web ontology language overview. http://www.w3.org/TR/owl-features/ Retrieved 2011–06-30

W3C (2007) Semantic annotations for wsdl and xml schema (sawsdl). http://www.w3.org/TR/sawsdl/ Retrieved 2011–06-30

W3C (2007) Soap version 1.2 part 1: messaging framework (second edition). http://www.w3.org/TR/soap12-part1/#intro. Retrieved 2011–06-30

W3C (2007) Web services description language (wsdl) version 2.0. http://www.w3.org/TR/wsdl20/ Retrieved 2011–06-30

Wikipedia. Beam search. http://en.wikipedia.org/wiki/Beam_search Retrieved 2011–09-02

Wikipedia. Dijkstra’s algorithm. http://en.wikipedia.org/wiki/Dijkstra’27_algorithm Retrieved 2011–06-30

Zheng X, Yan Y (2008) An efficient web service composition algorithm based on planning graph. In: Proceedings of ICWS, pp 691–699