TY - JOUR
T1 - Service Provisioning for UAV-Enabled Mobile Edge Computing
AU - Qu, Yuben
AU - Dai, Haipeng
AU - Wang, Haichao
AU - Dong, Chao
AU - Wu, Fan
AU - Guo, Song
AU - Wu, Qihui
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102200; in part by the National Natural Science Foundation of China under Grant 61931011, Grant 62072303, Grant 61872178, and Grant 62001514; in part by the National Postdoctoral Program for Innovative Talents of China under Grant BX20190202; in part by the Open Project Program of the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space under Grant KF20202105; and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20181251. The work of Song Guo was supported in part by the Hong Kong RGC Research Impact Fund (RIF) under Project R5034-18 and in part by the General Research Fund of the Research Grants Council of Hong Kong under Grant PolyU 152221/19E.
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.
AB - Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.
KW - edge computing
KW - network function virtualization
KW - Unmanned aerial vehicles (UAVs)
UR - http://www.scopus.com/inward/record.url?scp=85110843837&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2021.3088660
DO - 10.1109/JSAC.2021.3088660
M3 - Journal article
AN - SCOPUS:85110843837
SN - 0733-8716
VL - 39
SP - 3287
EP - 3305
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 11
ER -