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Energy Efficient Resource Scheduling Using Optimization Based Neural Network in Mobile Cloud Computing

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Abstract

The mobile cloud computing has become an emerging technology where the mobile computing is integrated with cloud computing to process the mobile data. Besides the advantages of mobile cloud computing, there are some issues which include power consumption, resource scarcity, quality of service, security and computational cost. In this paper, in order to minimize total power consumption with better performance, the neural network based optimization methods using artificial neural network and convolutional neural network models were implemented by varying variance and loudness. From the experimental results it is observed that, by using optimization in the neural network, the power consumption has been reduced by 53.68% and obtained improvement using convolutional neural network which further reduced the power consumption by 30.3% with minimum root mean square error compared with other algorithms.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore, Tamil Nadu, 632014, India

    Praveena Akki & V. Vijayarajan

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  1. Praveena Akki
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  2. V. Vijayarajan
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Correspondence to V. Vijayarajan.

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Akki, P., Vijayarajan, V. Energy Efficient Resource Scheduling Using Optimization Based Neural Network in Mobile Cloud Computing. Wireless Pers Commun 114, 1785–1804 (2020). https://doi.org/10.1007/s11277-020-07448-2

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