Mobility-Aware Multi-User Offloading Optimization for Mobile Edge Computing

Mobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks to lightweight and ubiquitously deployed MEC servers. In this paper, we investigate the problem of offloading decision and re...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-03, Vol.69 (3), p.3341-3356
Main Authors: Zhan, Wenhan, Luo, Chunbo, Min, Geyong, Wang, Chao, Zhu, Qingxin, Duan, Hancong
Format: Article
Language:English
Subjects:
Algorithms
Computation offloading
Computer simulation
Edge computing
Energy consumption
Global optimization
Heuristic methods
Integer programming
Local optimization
Mobile computing
Mobile edge computing
mobility-aware offloading
Numerical methods
Optimization
Polynomials
Resource allocation
Resource management
Servers
Task analysis
task offloading
Wireless communication
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Summary:Mobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks to lightweight and ubiquitously deployed MEC servers. In this paper, we investigate the problem of offloading decision and resource allocation among multiple users served by one base station to achieve the optimal system-wide user utility, which is defined as a trade-off between task latency and energy consumption. Mobility in the process of task offloading is considered in the optimization. We prove that the problem is NP-hard and propose a heuristic mobility-aware offloading algorithm (HMAOA) to obtain the approximate optimal offloading scheme. The original global optimization problem is converted into multiple local optimization problems. Each local optimization problem is then decomposed into two subproblems: a convex computation allocation subproblem and a non-linear integer programming (NLIP) offloading decision subproblem. The convex subproblem is solved with a numerical method to obtain the optimal computation allocation among multiple offloading users, and a partial order based heuristic approach is designed for the NLIP subproblem to determine the approximate optimal offloading decision. The proposed HMAOA is with polynomial complexity. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate its excellent performance.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2966500