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In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience (QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two aspects. First, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.


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QoE-Driven Big Data Management in Pervasive Edge Computing Environment

Show Author's information Qianyu MengKun Wang( )Xiaoming HeMinyi Guo
Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210003, and the Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Abstract

In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience (QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two aspects. First, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.

Keywords:

Quality-of-Experience (QoE), high-dimensional big data management, deep learning, pervasive edge computing
Received: 01 February 2018 Accepted: 14 February 2018 Published: 24 May 2018 Issue date: September 2018
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Publication history

Received: 01 February 2018
Accepted: 14 February 2018
Published: 24 May 2018
Issue date: September 2018

Copyright

© The author(s) 2018

Acknowledgements

This work was supported by the National Key Basic Research and Development (973) Program of China (No. 2015CB352401), the National Natural Science Foundation of China (Nos. 61572262 and 61772286), and China Postdoctoral Science Foundation (No. 2017M610252), China Postdoctoral Science Special Foundation (No. 2017T100297).

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