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Open Access

QoE-Driven Big Data Management in Pervasive Edge Computing Environment

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.
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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.


K. Wang, Y. Wang, Y. Sun, S. Guo, and J. Wu, Green industrial Internet of Things architecture: An energy-efficient perspective, IEEE Commun. Mag., vol. 54, no. 12, pp. 48-54, 2016.
M. Guo, E. Olule, G. Wang, and S. Guo, Designing energy efficient target tracking protocol with quality monitoring in wireless sensor networks, Journal of Supercomputing, vol. 51, no. 2, pp. 131-148, 2010.
Q. Meng, K. Wang, B. Liu, T. Miyazaki, and X. He, QoE-based big data analysis with deep learning in pervasive edge environment, in Proc. Int. IEEE Communications Conf., Kansas City, MO, USA, 2018.
X. Song, Y. Huang, Q. Zhou, F. Ye, Y. Yang, and X. Li, Pervasive edge data sharing in MANET, in Proc. Int. IEEE Computer Communications Workshops Conf., Atlanta, GA, USA, 2017, pp. 133-138.
C. Xu, J. Ren, Y. Zhang, Z. Qin, and K. Ren, DPPro: Differentially private high-dimensional data release via random projection, IEEE Trans. Information Forensics and Security, vol. 12, no. 12, pp. 3081-3093, 2017.
B. Wang and K. Mueller, The subspace voyager: Exploring high-dimensional data along a continuum of salient 3D subspaces, IEEE Trans. Visualization and Computer Graphics, vol. 24, no. 12, pp. 1204-1222, 2018.
Y. Chen, K. Wu, and Q. Zhang, From QoS to QoE: A tutorial on video quality assessment, IEEE Commun. Surveys & Tutorials, vol. 17, no. 2, pp. 1126-1165, 2014.
X. Zhou, K. Wang, W. Jia, and M. Guo, Reinforcement learning-based adaptive resource management of differentiated services in geo-distributed data centers, in Proc. Int. IEEE/ACM Symp. Quality of Service Conf., Vilanova, Spain, 2017, pp. 1-6.
Z. Ye, S. Mistry, A. Bouguettaya, and H. Dong, Long-term QoS-aware cloud service composition using multivariate time series analysis, IEEE Trans. Services Computing, vol. 9, no. 3, pp. 382-393, 2016.
S. Bulo, B. Biggio, I. Pillai, M. Pelillo, and F. Roli, Randomized prediction games for adversarial machine learning, IEEE Trans. Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2466-2478, 2017.
M. Li, J. Wei, X. Zheng, and M. Bolton, A formal machine-learning approach to generating human-machine interfaces from task models, IEEE Trans. Human-Machine Systems, vol. 47, no. 6, pp. 822-833, 2017.
H. Wu and S. Prasad, Semi-supervised deep learning using pseudo labels for hyperspectral image classification, IEEE Trans. Image Processing, vol. 27, no. 3, pp. 1259-1270, 2017.
Z. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, and K. Mizutani, State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems, IEEE Commun. Surveys & Tutorials, vol. 19, no. 4, pp. 2432-2455, 2017.
M. Federico, P. Julian, and P. Mandolesi, SCDVP: A simplicial CNN digital visual processor, IEEE Trans. Circuits and Systems I: Regular Papers, vol. 61, no. 7, pp. 1962-1969, 2014.
C. Hsu and C. Lin, CNN-based joint clustering and representation learning with feature drift compensation for large-scale image data, IEEE Trans. Multimedia, vol. 20, no. 2, pp. 421-429, 2017.
H. Lee, K. Hong, H. Kang, and S. Lee, Photo aesthetics analysis via DCNN feature encoding, IEEE Trans. Multimedia, vol. 19, no. 8, pp. 1921-1932, 2017.
P. Li, Z. Chen, L. Yang, Q. Zhang, and M. Deen, Deep convolutional computation model for feature learning on big data in internet of things, IEEE Trans. Industrial Informatics, .
R. Girshick, Fast R-CNN, in Proc. Int. IEEE Computer Vision Conf., Santiago, Chile, 2015, pp. 1440-1448.
Q. Chen, M. Guo, Q. Deng, L. Zheng, S. Guo, and Y. Shen, HAT: History-based auto-tuning MapReduce in heterogeneous environments, Journal of Supercomputing, vol. 64, no. 3, pp. 1038-1054, 2011.
T. Zhao, Q. Liu, and C. Chen, QoE in video transmission: A user experience-driven strategy, IEEE Commun. Surveys & Tutorials, vol. 19, no. 1, pp. 285-302, 2016.
S. Kim, G. Suk, J. Lee, and C. Chae, QoE-aware scalable video transmission in MIMO systems, IEEE Commun. Mag., vol. 55, no. 8, pp. 196-203, 2017.
C. Liang, Y. He, F. Yu, and N. Zhao, Enhancing QoE-aware wireless edge caching with software-defined wireless networks, IEEE Trans. Wireless Communications, vol. 16, no. 10, pp. 6912-6925, 2017.
K. Wang, L. Gu, S. Guo, H. Chen, V. Leung, and Y. Sun, Crowdsourcing-based content-centric network: A social perspective, IEEE Network, vol. 31, no. 5, pp. 28-34, 2017.
C. Ji, T. Dong, Y. Li, Y. Shen, K. Li, W. Qiu, W. Qu, and M. Guo, Inverted grid-based KNN query processing with MapReduce, in Proc. Int. IEEE ChinaGrid Annual Conf., Beijing, China, 2012, pp. 25-32.
S. Zhao and D. Medhi, Application-aware network design for Hadoop MapReduce optimization using software-defined networking, IEEE Trans. Network and Service Management, vol. 14, no. 4, pp. 804-816, 2017.
W. Shi, Y. Gong, X. Tao, J. Wang, and N. Zheng, Improving CNN performance accuracies with min-max objective, IEEE Trans. Neural Networks and Learning Systems, .
Z. Zhang, T. Weng, and L. Daniel, Big-data tensor recovery for high-dimensional uncertainty quantification of process variations, IEEE Trans. Components, Packaging and Manufacturing, vol. 7, no. 5, pp. 687-697, 2017.
M. Li and X. Wang, Delay and rate satisfaction for data transmission with application in wireless communications, IEEE Network, vol. 29, no. 5, pp. 70-75, 2015.
R. Borujeny, M. Noori, and M. Ardakani, Maximizing data rate for multiway relay channels with pairwise transmission strategy, IEEE Trans. Wireless Communications, vol. 16, no. 3, pp. 1609-1618, 2017.
K. Wang, X. Qi, L. Shu, D. Deng, and J. Rodrigues, Toward trustworthy crowdsourcing in social internet of things, IEEE Wireless Communications, vol. 30, no. 5, pp. 30-36, 2016.
D. Wu, B. He, X. Tang, J. Xu, and M. Guo, RAMZzz: Rank-aware DRAM power management with dynamic migrations and demotions, in Proc. Int. IEEE High Performance Computing, Networking, Storage and Analysis Conf., Salt Lake City, UT, USA, 2012, pp. 1-11.
C. Xu, K. Wang, and M. Guo, Intelligent resource management in blockchain based cloud data centers, IEEE Cloud Computing, vol. 4, no. 6, pp. 50-59, 2017.
Q. Chen, Y. Chen, Z. Huang, and M. Guo, WATS: Workload-aware task scheduling in asymmetric multi-core architectures, in Proc. Int. 26th IEEE Parallel and Distributed Processing Symposium Conf., Shanghai, China, 2012, pp. 249-260.
Z. Mei, Y. Zhang, X. Zhao, B. Jung, T. Sarkar, and M. Palma, Choice of the scaling factor in a marching-on-in-degree time domain technique based on the associated laguerre functions, IEEE Trans. Antennas and Propagation, vol. 60, no. 9, pp. 4463-4467, 2012.
H. Huang, P. Li, S. Guo, W. Liang, and K. Wang, Near-optimal deployment of service chains by exploiting correlations between network functions, IEEE Trans. Cloud Computing, .
G. Silva, R. Vieira, and C. Rech, Discrete-time sliding- mode observer for capacitor voltage control in modular multilevel converters, IEEE Trans. Industrial Electronics, vol. 65, no. 1, pp. 876-886, 2018.
R. Girshick, Photo aesthetics analysis via DCNN feature encoding, in Proc. Int. IEEE Computer Vision Conf., Santiago, Chile, 2015, pp. 1921-1932.
Y. Xu, J. Chen, and Q. Wang, The sum rate of vector gaussian multiple description coding with tree-structured covariance distortion constraints, IEEE Trans. Information Theory, vol. 63, no. 10, pp. 6747-6560, 2017.
K. Wang, J. Mi, C. Xu, Q. Zhu, L. Shu, and D. Deng, Real-time load reduction in multimedia big data for mobile Internet, ACM Trans. Multimedia Computing, Communications and Applications, vol. 12, no. 5s, p. 76, 2016.
K. Wang, H. Gao, X. Xu, J. Jiang, and D. Yue, An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks, IEEE Sensors Journal, vol. 16, no. 11, pp. 4051-4062, 2016.
K. Wang, Y. Shao, L. Shu, Y. Zhang, and C. Zhu, Mobile big data fault-tolerant processing for eHealth networks, IEEE Network, vol. 30, no. 1, pp. 1-7, 2016.
Z. Wang, J. Yang, R. Melhem, B. Childers, Y. Zhang, and M. Guo, Simultaneous multikernel GPU: Multi-tasking throughput processors via fine-grained sharing, in Proc. Int. IEEE Symp. High Performance Computer Architecture Conf., Barcelona, Spain, 2016, pp. 358-369.
X. He, K. Wang, T. Miyazaki, H. Huang, Y. Wang, and S. Guo, Green resource allocation based on deep reinforcement learning in content-centric IoT, IEEE Trans. Emerging Topics in Computing, .
Z. Li, Y. Shen, B. Yao, and M. Guo, OFScheduler: A dynamic network optimizer for MapReduce in heterogeneous cluster, International Journal of Parallel Programming, vol. 43, no. 3, pp. 472-488, 2013.
X. He, K. Wang, H. Huang, and B. Liu, QoE-driven big data architecture for smart city, IEEE Commun. Mag., vol. 56, no. 2, pp. 2-8, 2018.
W. Rahman, D. Yun, and K. Chung, A client side buffer management algorithm to improve QoE, IEEE Trans. Consumer Electronics, vol. 62, no. 4, pp. 371-379, 2016.
Big Data Mining and Analytics
Pages 222-233
Cite this article:
Meng Q, Wang K, He X, et al. QoE-Driven Big Data Management in Pervasive Edge Computing Environment. Big Data Mining and Analytics, 2018, 1(3): 222-233.








Web of Science






Received: 01 February 2018
Accepted: 14 February 2018
Published: 24 May 2018
© The author(s) 2018