Abstract
Web service composition aims to loosely couple web services to accommodate complex goals, which can not be accomplished by any existing web service. Many researchers have been working on such service composition problems with the aim to find composite services with optimized Quality of Service (QoS) and/or Quality of Semantic Matchmaking (QoSM). Due to the huge search space of this NP-hard problem, Evolutionary Computation techniques have been popularly utilized to search for solutions with near-optimal QoS and QoSM. A majority of these works share a common assumption that QoS of web services seldom or never changes. However, the execution of composite services obtained from the design stage may fail due to unexpected service failures at the execution stage. In this paper, we introduce a robust service composition approach with the goal to build robust composite services that serve as the blueprint/baseline for service execution. These baseline composite services can cope with unexpected interruptions in a robust manner, by applying local search to resume their feasibility while maintaining high quality at the time of execution. Our experiments show that our new approach can significantly outperform a state-of-the-art service composition method (without explicitly considering the robustness) in terms of both effectiveness and efficiency in the event of unexpected service failures.
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Wang, C., Ma, H., Chen, G., Hartmann, S. (2019). Towards Robust Web Service Composition with Stochastic Service Failures Based on a Genetic Algorithm. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_36
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