Open Access
ARTICLE
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. https://doi.org/10.32604/jbd.2020.010958
Received 10 April 2020; Accepted 31 August 2020; Issue published 13 October 2020
Abstract
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.
Keywords
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. https://doi.org/10.32604/jbd.2020.010958