Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing

Nan Li*, Alexandros Iosifidis, Qi Zhang

*Corresponding author for this work

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use early-exit mechanism to terminate the computation earlier to meet the deadline of inference tasks. We design a reward function to trade off the communication, computation and inference accuracy, and formu-late the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput in long term. To solve the maxi-mization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios. The experimental results show that G RLE achieves the average accuracy up to 3.41 x over graph reinforcement learning (GRL) and 1.45x over DROOE, which shows the advantages of GRLE for offloading decision-making in dynamic MEC.

Original languageEnglish
Title of host publication2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
Number of pages6
Publication date2022
ISBN (Electronic)978-1-6654-3540-6
Publication statusPublished - 2022
EventIEEE Global Communications Conference 2022: Hybrid: In-Person and Virtual Conference Accelerating the Digital Transformation through Smart Communications - Rio de Janeiro, Brazil
Duration: 4 Dec 20228 Dec 2022


ConferenceIEEE Global Communications Conference 2022
CityRio de Janeiro
Internet address


  • CNN infer-ence
  • Dynamic computation offloading
  • Edge computing
  • Graph reinforcement learning
  • Service reliability


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