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Workload Allocation Based on User Mobility in Mobile Edge Computing

Tengfei Yang1,2, Xiaojun Shi3, Yangyang Li1,*, Binbin Huang4, Haiyong Xie1,5, Yanting Shen4

1 National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (NEL-PSRPC), Beijing, China
2 National Computer Network Emergency Response Technical Team Coordination Center of China, Beijing, China
3 Department of Science and Technology, China Electronics Technology Group Corporation, Beijing, China
4 School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
5 University of Science and Technology of China, Hefei, China

* Corresponding Author: Yangyang Li. Email:

Journal on Big Data 2020, 2(3), 105-115.


Mobile Edge Computing (MEC) has become the most possible network architecture to realize the vision of interconnection of all things. By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations (sBSs), the execution latency and device power consumption can be reduced on resource-constrained mobile devices. However, computation delay of Mobile Edge Network (MEN) tasks are neglected while the unloading decision-making is studied in depth. In this paper, we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user. We obtain the next possible location through the user's past location information, and receive the next access server according to the grid matrix. Furthermore, the next time task sequence is calculated on the base of the historical time task sequence, and the server is chosen to preload the task. In the experiments, the results demonstrate a high accuracy of our proposed model.


Cite This Article

T. Yang, X. Shi, Y. Li, B. Huang, H. Xie et al., "Workload allocation based on user mobility in mobile edge computing," Journal on Big Data, vol. 2, no.3, pp. 105–115, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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