Tengfei Yang1,2, Xiaojun Shi3, Yangyang Li1,*, Binbin Huang4, Haiyong Xie1,5, Yanting Shen4
Journal on Big Data, Vol.2, No.3, pp. 105-115, 2020, DOI:10.32604/jbd.2020.010958
- 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 More >
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