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Failure recovery in distributed model composition with intelligent assistance

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Abstract

Composite models in Decision Support Systems (DSS) are combinations of model functions to solve complex decision problems. They must be executed successfully to obtain the desired results. Unfortunately, faults may happen during its execution due to the dynamic networks or the information asymmetry between developers and users. Therefore, designing effective failure recovery mechanism to ensure the reliability of the composite model execution is essential. Progress has been made in the web service composition field to obtain limited failure recovery capabilities, but not fully applicable to composite models since model services are informational services in essence which do not change the world conditions. This paper proposes a unified framework, integrated with a process ontology and multiple recovery strategies that can provide valuable failure recovery recommendations intelligently. The recovery strategies have been greatly enhanced in providing greater failure recovery capability. Feasibility and efficiency of this framework have been illustrated and tested.

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Notes

  1. NuPDDL: nondeterminism and more in PDDL: http://mbp.fbk.eu/NuPDDL.html

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Acknowledgments

This work is jointly supported by National Natural Science Foundations of China (No.60903174, 61142010), the Fundamental Research Funds for the Central Universities, HUST: 2012QN087, 2012QN088 and the Fund of Key Lab for Image Processing and Intelligent Control (20093).

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Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hui Huang & Xueguang Chen

  2. Department of Computer Science and Engineering, Henan Institute of Engineering, Zhengzhou, 451191, China

    Zhiwu Wang

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  1. Hui Huang
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Correspondence to Hui Huang.

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Huang, H., Chen, X. & Wang, Z. Failure recovery in distributed model composition with intelligent assistance. Inf Syst Front 17, 673–689 (2015). https://doi.org/10.1007/s10796-013-9464-3

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