RL-based Computation Offloading Scheme for Improving QoE in Edge Computing Environments
Jinho Park, Kwang-Sue Chung · 2023
With the rapid development of Internet of Things (IoT) devices and networks, various services are being provided to people. However, IoT devices are limited by computing resources and cannot satisfy services that require short processing time. To satisfy the requirements of these services, edge collaboration schemes have been researched to utilize the computing resources of other edge servers. Generally, edge collaboration schemes offload tasks received from devices to appropriate edge servers based on Reinforcement Learning (RL). Reinforcement learningbased edge collaboration schemes do not exhibit high performance due to decision-making based on a single objective function. In this paper, we propose a RL-based computation offloading scheme for improving QoE in edge computing environments. The proposed scheme approximates the action distribution based on the multiobjective function to maximize performance. To evaluate the performance of the proposed scheme, comparative experiments were conducted in simulation environment, comparing it with existing computation offloading schemes that utilize reinforcement learning in edge collaboration environments. The experimental results confirm the improved learning performance compared to the existing schemes.