On October 23rd, 2014, we updated our
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Adaptation as Planning Problem obtaining the Adaptation Process (Madapt) We exploit the ASTRO automated composition approach - www.astroproject.org Sophisticated AI planning techniques (Planning as Model Checking) Asynchronous domains, non-determinism, partial observability Complex goals: preferences and recovery conditions (EaGle) Control and data flow composition requirements Research work on ASTRO automated service composition: Annapaola Marconi, Marco Pistore: Synthesis and Composition of Web Services. SFM 2009: 89-157 Annapaola Marconi, Marco Pistore, Paolo Traverso: Automated Composition of Web Services: the ASTRO Approach. IEEE Data Eng. Bull. 31(3): 23-26 (2008) Annapaola Marconi, Marco Pistore, Piero Poccianti, Paolo Traverso: AutomatedWeb Service Composition at Work: the Amazon/MPS Case Study. ICWS 2007: 767-774. Annapaola Marconi, Marco Pistore, Paolo Traverso: Specifying Data-Flow Requirements for the Automated Composition of Web Services. SEFM 2006: 147-156 M. Pistore, P. Traverso, P. Bertoli, and A. Marconi, Automated synthesis of composite BPEL4WS web services” in Proc. ICWS 2005
Evaluation (2) planning performance The performance of the planning algorithm was tested on a 2GHz, 3Gb Dual Core machine running Windows. In our experiments, the delay caused by planning and adaptation process never exceeded 4 seconds. Given the high complexity of the scenario, it demonstrates The practical applicability of our approach
Evaluation (2) modeling overhead In order to evaluate the modeling effort of our approach we implemented the same adaptation mechanism using a rule-based approach. We defined 35 rules and verified the whole rule-based system. In order to compare scalability of our approach respect to rule-based, we simulated the replacement of a service by a new one with the same functionality but different usage policies. Our approach required only proper annotation of a new service The rule-based implementation required modifying 3 rules and re- verifying the whole rule system. Our approach proved to be considerably more scalable than rule-based and built-in
Conclusions We a presented a novel approach to adapt business processes where: running application and the adaptation logic are two separate components Adaptation activities are generated at runtime, when a problem arises (i.e., Dynamic Adaptation) Future Work To extend the framework to consider also abstract tasks of the business processes The refinement of an abstract activity with executable service composition is done automatically and at runtime taking into account the current status of the execution environment. We plan to consider other adaptation strategies, e.g., to roll the execution back to a branching point in an attempt to take different branches. We plan to use the execution history of adapted instances as a training set to progressively improve the process model (Process Evolution)