Dynamic Workflow Composition Using Markov Decision Processes

The advent of Web services has made automated workflow composition possible. One technique, that has received some attention, for automatically composing workflows is AIbased classical planning. However, classical planning suffers from the paradox of first assuming deterministic behavior of Web services, then requiring the additional overhead of execution monitoring to recover from unexpected behavior of services. To address these concerns, we propose utilizing Markov decision processes (MDPs), an efficient stochastic optimization framework, to model workflow composition. Our method models both, the inherent stochastic nature of Web services, and the dynamic nature of the environment. The resulting workflows are robust to non-deterministic behaviors of Web services and adaptive to a changing environment. Using an example scenario, we demonstrate our method and provide empirical results in its support.

By: Prashant Doshi, Richard Goodwin, Rama Akkiraju, Kunal Verma

Published in: RC23134 in 2004


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