Improving offload delay using flow splitting and aggregation in edge computing
Author:
Affiliation:
1. Dept. of Electrical Engineering, Tokyo University of Science
2. Dept. of Information Systems Engineering, The University of Kitakyushu
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/comex/8/12/8_2019GCL0009/_pdf
Reference9 articles.
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2. [2] W. Yu, F. Liang, X. He, W. G. Hatcher, C. Lu, J. Lin, and X. Yang, “A survey on the edge computing for the Internet of Things,” IEEE Access, vol. 6, pp. 6900–6919, Nov. 2017. DOI:10.1109/ACCESS.2017.2778504
3. [3] T. Zhao, S. Zhou, X. Guo, Y. Zhao, and Z. Niu, “A cooperative scheduling scheme of local cloud and Internet cloud for delay-aware mobile cloud computing,” Proc. IEEE GLOBECOM2015, pp. 1–6, Dec. 2015. DOI:10.1109/GLOCOMW.2015.7414063
4. [4] M. Chen and Y. Hao, “Task offloading for mobile edge computing in software defined ultra-dense network,” IEEE J. Sel. Areas Commun., vol. 36, no. 3, pp. 587–597, Mar. 2018. DOI:10.1109/JSAC.2018.2815360
5. [5] Y. Hao, M. Chen, L. Hu, M. S. Hossain, and A. Ghoneim, “Energy efficient task caching and offloading for mobile edge computing,” IEEE Access, vol. 6, pp. 11365–11373, Mar. 2018. DOI:10.1109/ACCESS.2018.2805798
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