TY  - EJOU
AU  - Xiong, Ao 
AU  - Chen, Meng 
AU  - Guo, Shaoyong 
AU  - Li, Yongjie 
AU  - Zhao, Yujing 
AU  - Ou, Qinghai 
AU  - Liu, Chuan 
AU  - Xu, Siwen 
AU  - Liu, Xiangang 

TI  - An Energy Aware Algorithm for Edge Task Offloading
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 31
IS  - 3
SN  - 2326-005X

AB  - To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of the edge environment, this paper designs a task unloading algorithm based on Proximal Policy Optimization (PPO), besides we use Dijkstra to determine the connection path between edge servers where adjacent tasks are deployed. Finally, lots of simulation experiments verify the effectiveness of the proposed method in the process of task unloading. Compared with contrast algorithms, the average energy saving of the proposed algorithm can reach 22.69%.
KW  - Edge computing; energy consumption; task offloading; reinforcement learning

DO  - 10.32604/iasc.2022.018881