QoE-Driven Big Data Management in Pervasive Edge Computing Environment
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 i...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020020 |
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| author | Qianyu Meng Kun Wang Xiaoming He Minyi Guo |
| author_facet | Qianyu Meng Kun Wang Xiaoming He Minyi Guo |
| author_sort | Qianyu Meng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7339ece5b76b4f22bfc47047a9413858 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-7339ece5b76b4f22bfc47047a94138582025-02-02T06:49:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-09-011322223310.26599/BDMA.2018.9020020QoE-Driven Big Data Management in Pervasive Edge Computing EnvironmentQianyu Meng0Kun Wang1Xiaoming He2Minyi Guo3<institution content-type="dept">Jiangsu Engineering Research Center of Communication and Network Technology</institution>, <institution>Nanjing University of Posts and Telecommunications</institution>, <city>Nanjing</city> <postal-code>210003</postal-code>, <country>China</country>.<institution content-type="dept">Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks</institution>, <institution>Nanjing University of Posts and Telecommunications</institution>, <city>Nanjing</city> <postal-code>210003</postal-code>, and the <institution content-type="dept">Department of Computer Science and Engineering</institution>, <institution>Shanghai Jiao Tong University</institution>, <city>Shanghai</city> <postal-code>200240</postal-code>, <country>China</country>.<institution content-type="dept">Jiangsu Engineering Research Center of Communication and Network Technology</institution>, <institution>Nanjing University of Posts and Telecommunications</institution>, <city>Nanjing</city> <postal-code>210003</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science and Engineering</institution>, <institution>Shanghai Jiao Tong University</institution>, <city>Shanghai</city> <postal-code>200240</postal-code>, <country>China</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020020quality-of-experience (qoe)high-dimensional big data managementdeep learningpervasive edge computing |
| spellingShingle | Qianyu Meng Kun Wang Xiaoming He Minyi Guo QoE-Driven Big Data Management in Pervasive Edge Computing Environment Big Data Mining and Analytics quality-of-experience (qoe) high-dimensional big data management deep learning pervasive edge computing |
| title | QoE-Driven Big Data Management in Pervasive Edge Computing Environment |
| title_full | QoE-Driven Big Data Management in Pervasive Edge Computing Environment |
| title_fullStr | QoE-Driven Big Data Management in Pervasive Edge Computing Environment |
| title_full_unstemmed | QoE-Driven Big Data Management in Pervasive Edge Computing Environment |
| title_short | QoE-Driven Big Data Management in Pervasive Edge Computing Environment |
| title_sort | qoe driven big data management in pervasive edge computing environment |
| topic | quality-of-experience (qoe) high-dimensional big data management deep learning pervasive edge computing |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020020 |
| work_keys_str_mv | AT qianyumeng qoedrivenbigdatamanagementinpervasiveedgecomputingenvironment AT kunwang qoedrivenbigdatamanagementinpervasiveedgecomputingenvironment AT xiaominghe qoedrivenbigdatamanagementinpervasiveedgecomputingenvironment AT minyiguo qoedrivenbigdatamanagementinpervasiveedgecomputingenvironment |