eB 12(1): e2

Research Article

The Implementation of A Dependency Matrix-based QoS Diagnosis Support in SOA Middleware

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  • @ARTICLE{10.4108/eb.2012.07-09.e2,
        author={Jing Zhang and Xiaoqi Zhang and Yi-Chin Chang and Kwei-Jay Lin},
        title={The Implementation of A Dependency Matrix-based QoS Diagnosis Support in SOA Middleware},
        journal={EAI Endorsed Transactions on e-Business},
        volume={1},
        number={1},
        publisher={ICST},
        journal_a={EB},
        year={2012},
        month={9},
        keywords={SOA, Middleware, QoS, Diagnosis},
        doi={10.4108/eb.2012.07-09.e2}
    }
    
  • Jing Zhang
    Xiaoqi Zhang
    Yi-Chin Chang
    Kwei-Jay Lin
    Year: 2012
    The Implementation of A Dependency Matrix-based QoS Diagnosis Support in SOA Middleware
    EB
    ICST
    DOI: 10.4108/eb.2012.07-09.e2
Jing Zhang1,*, Xiaoqi Zhang1, Yi-Chin Chang1, Kwei-Jay Lin1
  • 1: Department of Electrical Engineering and Computer Science, University of California Irvine,Irvine, CA 92697-2625, USA
*Contact email: zhangjing00@gmail.com

Abstract

When an SOA business process fails to deliver the desired quality of service (QoS), it is necessary to identify the faulty services that cause the problem since the source of the problem may not be at where the problem is observed. In this paper, we propose a polynomial time diagnosis algorithm by using a dependency matrix for business process structure in SOA. The dependency matrix is built based only on process workflow structure, with no need for historical knowledge on prior executionx. By comparing the performance data reported from business process probes, the proposed diagnosis algorithm also checks some predicates-on-probes (PoP) to increase the monitoring and diagnosis accuracy. We have implemented the diagnosis support for the dependency matrix based QoS management in the Llama middleware. A performance study using some realistic services running on networked Web servers shows that the system can achieve a diagnosis completeness of up to 80%.