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    Evolutionary approaches to signal decomposition in an application service management system

    Sikora, T.D. and Magoulas, George D. (2016) Evolutionary approaches to signal decomposition in an application service management system. Soft Computing 20 (8), pp. 3063-3084. ISSN 1432-7643.

    ASM-ActSignExtract_SoftComp2015_v2.pdf - Author's Accepted Manuscript

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    The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.


    Item Type: Article
    Keyword(s) / Subject(s): Application Performance Management, Application Service Management, Autonomous Con- trol, Data Analysis, Data Modeling, Metaheuristics, Multidimensional Deconvolution, Optimization, Signal Extraction, Service Level Agreement
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: George Magoulas
    Date Deposited: 22 Dec 2015 11:31
    Last Modified: 09 Aug 2023 12:37


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