Workload Allocation Based on User Mobility in Mobile Edge Computing  

作　　者：Tengfei Yang Xiaojun Shi Yangyang Li Binbin Huang Haiyong Xie Yanting Shen 

机构地区：[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

出　　处：《Journal on Big Data》2020年第3期105-115,共11页大数据杂志（英文）

基　　金：This work is supported by the CETC Joint Advanced Research Foundation(No.6141B08020101);Major Special Science and Technology Project of Hainan Province(No.ZDKJ2019008).

摘　　要：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.

关 键 词：Edge computing workload allocation social-LSTM CONTEXT-SENSITIVE user mobility 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]