TY - GEN
T1 - Energy-efficient computation offloading with privacy preservation for edge computing-enabled 5G networks
AU - Liu, Xihua
AU - Xu, Xiaolong
AU - Yuan, Yuan
AU - Zhang, Xuyun
AU - Dou, Wanchun
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Nowadays, due to the developments in wireless communication, the amount of data produced by mobile devices is increasing rapidly. The mobile devices can hardly handle these data immediately as they have limitations on their computing power. In edge computing, the computing tasks can be offloaded from the mobile devices to nearby edge nodes (ENs) for implementing. Combined with 5G networks, the computing tasks can be offloaded to the central units (CUs), enhanced into ENs, or the cloud infrastructure via distributed units (DUs) for processing. In this way, the above phenomenon will be effectively released. However, how to select the appropriate ENs for executing, aiming to keep a balance between the load balance and the energy consumption, is still a big problem waiting to be solved. In this paper, an optimization problem is formulated to improve the load balance and reduce the energy consumption of all the ENs for edge computing-enabled 5G networks while considering the privacy conflicts and time consumption. Then, an energy-efficient computation offloading method with privacy preservation, named ECOP, is proposed. Finally, experimental results and evaluations confirm our proposed method is feasible.
AB - Nowadays, due to the developments in wireless communication, the amount of data produced by mobile devices is increasing rapidly. The mobile devices can hardly handle these data immediately as they have limitations on their computing power. In edge computing, the computing tasks can be offloaded from the mobile devices to nearby edge nodes (ENs) for implementing. Combined with 5G networks, the computing tasks can be offloaded to the central units (CUs), enhanced into ENs, or the cloud infrastructure via distributed units (DUs) for processing. In this way, the above phenomenon will be effectively released. However, how to select the appropriate ENs for executing, aiming to keep a balance between the load balance and the energy consumption, is still a big problem waiting to be solved. In this paper, an optimization problem is formulated to improve the load balance and reduce the energy consumption of all the ENs for edge computing-enabled 5G networks while considering the privacy conflicts and time consumption. Then, an energy-efficient computation offloading method with privacy preservation, named ECOP, is proposed. Finally, experimental results and evaluations confirm our proposed method is feasible.
KW - 5G
KW - edge computing
KW - edge nodes
KW - energy consumption
KW - load balance
UR - http://www.scopus.com/inward/record.url?scp=85074845559&partnerID=8YFLogxK
U2 - 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00050
DO - 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00050
M3 - Conference proceeding contribution
SN - 9781728129815
T3 - Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
SP - 176
EP - 181
BT - Proceedings - 2019 IEEE International Congress on Cybermatics
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
Y2 - 14 July 2019 through 17 July 2019
ER -