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Wireless Communications and Mobile Computing
Volume 2018, Article ID 3794175, 13 pages
https://doi.org/10.1155/2018/3794175
Research Article

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

1State Key Laboratory of Marine Resource Utilization in the South China Sea, College of Information Science and Technology, Hainan University, Haikou, China
2School of Computer Science and Engineering, Tianjin University, Tianjin, China
3School of Information Engineering, Yangzhou University, Yangzhou, China
4Computing Center, Shanghai University, Shanghai, China
5Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai, China

Correspondence should be addressed to Yucong Duan; moc.liamtoh@gnocuynaud

Received 27 January 2018; Accepted 16 April 2018; Published 30 May 2018

Academic Editor: Xuyun Zhang

Copyright © 2018 Zhengyang Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based three-tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.