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Multi-objective Parametric Query Optimization for Distributed Database Systems

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 436))

Abstract

A classical query optimization compares solutions on single cost metric, not capable for multiple costs. A multi-objective parametric optimization (MPQ) approach is potentially capable for optimization over multiple cost metrics and query parameters. This paper demonstrated an approach for multi-objective parametric query optimization (MPQO) for advanced database systems such as distributed database systems (DDBS). The query equivalent plans are compared according to multiple cost metrics and query related parameters (modeled by a function on metrics), cost metrics, and query parameters are semantically different and computed at different stage of optimization. MPQO also generalizes parametric optimization by catering the multiple metrics for query optimization. In this paper, performance of MPQO variants based on nature-inspired optimization; ‘Multi-Objective Genetic Algorithm’ and a parameter-less optimization ‘Teaching-learning- based optimization’ are also analyzed. MPQO builds a parametric space of query plans and progressively explores the multi-objective space according to user tradeoffs on query metrics. In heterogeneous and distributed database system, logically unified data is replicated and distributed across multiple distributed sites to achieve high reliable and available data system; this imposed a challenge on evaluation of Pareto set. An MPQO attempt exhaustively determines the optimal query plans on each end of parametric space.

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Acknowledgments

The author was inspired (in different ways) by discussion at the AEOTIT 2014, a workshop on advanced optimization techniques had the discussions that ultimately led to this research work. Dr. R.V. Rao, Professor at SVNIT Surat, Gujrat, India. The TLBO played key role on the development of optimized solution in various fundamental engineering problems.

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Authors and Affiliations

  1. National Institute of Technology, Kurukshetra, 139119, Haryana, India

    Vikram Singh

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  1. Vikram Singh
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Correspondence to Vikram Singh .

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Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Chankyapuri, Rm 327, South Asian Univ, Akbar Bhawan, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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Vikram Singh (2016). Multi-objective Parametric Query Optimization for Distributed Database Systems. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_18

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  • DOI: https://doi.org/10.1007/978-981-10-0448-3_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0447-6

  • Online ISBN: 978-981-10-0448-3

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