Edge computational task offloading scheme using reinforcement learning for IIoT scenario
Md. Sajjad Hossain,
Cosmas Ifeanyi Nwakanma,
Jae Min Lee,
Dong-Seong Kim
Affiliations
Md. Sajjad Hossain
Networked Systems Lab., Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 3917, South Korea
Cosmas Ifeanyi Nwakanma
Networked Systems Lab., Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 3917, South Korea
Jae Min Lee
Networked Systems Lab., Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 3917, South Korea
Dong-Seong Kim
Corresponding author.; Networked Systems Lab., Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 3917, South Korea
In this paper, end devices are considered here as agent, which makes its decisions on whether the network will offload the computation tasks to the edge devices or not. To tackle the resource allocation and task offloading, paper formulated the computation resource allocation problems as a sum cost delay of this framework. An optimal binary computational offloading decision is proposed and then reinforcement learning is introduced to solve the problem. Simulation results demonstrate the effectiveness of this reinforcement learning based scheme to minimize the offloading cost derived as computation cost and delay cost in industrial internet of things scenarios.
