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

• Published in: Wireless personal communications 2020-09, Vol.114 (2), p.1785-1804
• Main Authors: Akki, Praveena, Vijayarajan, V.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Cloud computing; Communications Engineering; Computer Communication Networks; Computing costs; Engineering; Loudness; Mobile computing; Networks; Neural networks; New technology; Optimization; Power consumption; Resource scheduling; Signal,Image and Speech Processing; Wireless networks
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-020-07448-2

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.