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MLP-ANN-Based Execution Time Prediction Model and Assessment of Input Parameters Through Structural Modeling

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Proceedings of the National Academy of Sciences, India Section A: Physical Sciences Aims and scope Submit manuscript

Abstract

A multilayer perceptron–artificial neural network (MLP-ANN)-based prediction model is proposed to predict the execution time of tasks in cloud environment. Significant input parameters are identified and selected through interpretive structural modeling (ISM) approach. A prediction model is proposed for predicting the task execution time for varying number of inputs. The proposed model is validated and provides 21.7% reduction in mean relative error compared to other state-of-the-art methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, MP, India

    Anju Shukla, Shishir Kumar & Harikesh Singh

Authors
  1. Anju Shukla
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  2. Shishir Kumar
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  3. Harikesh Singh
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Correspondence to Anju Shukla.

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Significance Statement

Cloud computing offers on demand resource provisioning and allocation based on pay-per-use model. Sometimes prediction of execution times becomes necessity for reduction in various performance metrics. The accuracy of any prediction model depends on the input data that are fed into the network. Therefore, various related parameters are identified which may affect the output of prediction model. Based on that, an execution time prediction model is proposed which uses two techniques: interpretive structural modeling (ISM) and artificial neural network (ANN). ISM is used to identify the parameters that influence on execution time. ANN is used to predict the execution time.

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Shukla, A., Kumar, S. & Singh, H. MLP-ANN-Based Execution Time Prediction Model and Assessment of Input Parameters Through Structural Modeling. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 91, 577–585 (2021). https://doi.org/10.1007/s40010-020-00695-9

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  • DOI: https://doi.org/10.1007/s40010-020-00695-9

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