@Article{jbd.2020.010958,
AUTHOR = {Tengfei Yang, Xiaojun Shi, Yangyang Li, Binbin Huang, Haiyong Xie, Yanting Shen},
TITLE = {Workload Allocation Based on User Mobility in Mobile Edge Computing},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {105--115},
URL = {http://www.techscience.com/jbd/v2n3/40327},
ISSN = {2579-0056},
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.},
DOI = {10.32604/jbd.2020.010958}
}