TY - GEN
T1 - Resource Scheduling and Offloading Strategy Based on LEO Satellite Edge Computing
AU - Wei, Kaixiang
AU - Tang, Qingqing
AU - Guo, Jing
AU - Zeng, Ming
AU - Fei, Zesong
AU - Cui, Qimei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The Satellite communication network is not limited by geographical factors and is an indispensable part of global interconnection. This paper analyzes a hybrid of cloud computing and edge computing LEO satellite network architecture, where the three-tier network is composed of ground users, LEO satellites, and remote cloud servers. Users can not only forward computing tasks to remote cloud servers through LEO satellites for processing, but also directly offload computing tasks to LEO satellites for processing. Based on this, we study the problem of user computing offloading in LEO satellite network and construct a joint optimization problem of offloading decision and computing resource allocation, which aims to reduce the user processing delay and energy consumption when the total computing resources of edge nodes are limited. This problem is a mixed-integer nonlinear problem, which is difficult to be solved in finite time. With the increase in the number of users, the complexity of the solution is very high. Therefore, we reconstruct the problem based on the linear reconstruction technique and relax the binary variables to transform the original non-convex problem into a convex problem. The simulation results show that the algorithm can effectively reduce user delay and energy consumption compared to the case when the tasks are processed locally.
AB - The Satellite communication network is not limited by geographical factors and is an indispensable part of global interconnection. This paper analyzes a hybrid of cloud computing and edge computing LEO satellite network architecture, where the three-tier network is composed of ground users, LEO satellites, and remote cloud servers. Users can not only forward computing tasks to remote cloud servers through LEO satellites for processing, but also directly offload computing tasks to LEO satellites for processing. Based on this, we study the problem of user computing offloading in LEO satellite network and construct a joint optimization problem of offloading decision and computing resource allocation, which aims to reduce the user processing delay and energy consumption when the total computing resources of edge nodes are limited. This problem is a mixed-integer nonlinear problem, which is difficult to be solved in finite time. With the increase in the number of users, the complexity of the solution is very high. Therefore, we reconstruct the problem based on the linear reconstruction technique and relax the binary variables to transform the original non-convex problem into a convex problem. The simulation results show that the algorithm can effectively reduce user delay and energy consumption compared to the case when the tasks are processed locally.
KW - LEO satellite communication network
KW - convex optimization
KW - edge computing
UR - http://www.scopus.com/inward/record.url?scp=85122995804&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Fall52928.2021.9625072
DO - 10.1109/VTC2021-Fall52928.2021.9625072
M3 - Conference contribution
AN - SCOPUS:85122995804
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Y2 - 27 September 2021 through 30 September 2021
ER -