Open AccessEnergy-Efficient Joint Computation Offloading and Resource Allocation in Multi-User MEC Systems
Author(s) -
Yao Meng,
Janxin Dai
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012042
Subject(s) - mobile edge computing , computer science , computation offloading , benchmark (surveying) , energy consumption , distributed computing , enhanced data rates for gsm evolution , computation , task (project management) , resource allocation , edge computing , user equipment , mobile device , computer network , base station , algorithm , engineering , artificial intelligence , geodesy , systems engineering , electrical engineering , geography , operating system
Mobile edge computing (MEC) is envisioned as an emerging paradigm to enable energy-constrained and computation-limited user equipments (UEs) to offload various computation tasks to the edge of mobile networks in order to save energy consumption and prolong the battery life of UEs. In this paper, we consider a multi-user MEC system where each UE has a computation task to be processed locally or offloaded for remote processing. Specifically, we investigate the task offloading problem with the aim of minimizing the energy consumption of UEs via jointly optimizing the task admission decision, the transmission power, local computing and edge computing capacities. To solve this nonconvex problem, we transform it into four subproblems and propose a low-complexity algorithm by solving subproblems in an iterative manner. Simulation results demonstrate that the proposed algorithm requires lower energy consumption than the existing benchmark schemes.