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Workload Prediction of E-business Websites on Cloud Using Different Methods of ANN

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Big Data Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 654))

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Abstract

Workload forecasting of cloud-based application depends on the type of application and user behavior. Measurement of workload can be done in terms of loads, data storage, service rate, processing time, etc. In this paper, author has tried to predict workload of e-business website on cloud-based environment. ANN-based approach is used by author to calculate number of cloud instances required to manage workload efficiently. Also different training method of ANN has been applied to perform comparative study. MATLAB neural network toolbox is used for simulation work. An Amazon cloud service is also used for different parameters of data collection.

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Websites

  1. http://in.mathworks.com/products/neural-network/

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Author information

Authors and Affiliations

  1. Research Scholar, JNU, Jaipur, India

    Supreet Kaur Sahi

  2. Department of Computer Science and Engineering, JNU, Jaipur, India

    V. S. Dhaka

Authors
  1. Supreet Kaur Sahi
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  2. V. S. Dhaka
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Corresponding author

Correspondence to Supreet Kaur Sahi .

Editor information

Editors and Affiliations

  1. Jagan Institute of Management Studies, New Delhi, Delhi, India

    V. B. Aggarwal

  2. Department of Computer Science, University of Delhi, New Delhi, Delhi, India

    Vasudha Bhatnagar

  3. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

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© 2018 Springer Nature Singapore Pte Ltd.

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Sahi, S.K., Dhaka, V.S. (2018). Workload Prediction of E-business Websites on Cloud Using Different Methods of ANN. In: Aggarwal, V., Bhatnagar, V., Mishra, D. (eds) Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-10-6620-7_10

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  • DOI: https://doi.org/10.1007/978-981-10-6620-7_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6619-1

  • Online ISBN: 978-981-10-6620-7

  • eBook Packages: EngineeringEngineering (R0)

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