Deep Reinforcement Learning-Based Task Partitioning Ratio Decision Mechanism in High-Speed Rail Environments with Mobile Edge Computing Server - Seolwon Koo; Yujin Lim (Applied Sciences)
High-speed rail (HSR) environments present unique challenges due to their high mobility and dense passenger traffic, resulting in dynamic and unpredictable task generation patterns. Mobile Edge Computing (MEC) has emerged as a transformative paradigm to address these challenges by deploying computation resources closer to end-users. However, the limited resources of MEC servers necessitate efficient task partitioning, wherein a single task is divided into multiple sub-tasks for parallel processing across MEC servers. In the context of HSR environments, the task partitioning ratio is pivotal in ensuring quality of service (QoS) and optimizing resource utilization, particularly under dynamic and high-demand conditions. This paper proposes a deep reinforcement learning (DRL)-based task partitioning mechanism using Twin Delayed Deep Deterministic Policy Gradient (TD3) for HSR environments with MEC servers (MECSs). The proposed method dynamically adjusts task partitioning ratios by leveraging real-time information about task characteristics and server load conditions. The experimental results show that when the task arrival rate is 20, the delay is improved by about 5% compared to random and about 13% compared to no_partition. When it is 50, there is no significant difference from random and about 2% improvement compared to no_partition. The task throughput is almost the same when it is 20. However, when it is 50, random is much better. We also looked at the performance change according to the number of serving MECSs. In this process, we can also note the research direction of finding an appropriate number of serving MECSs K. The results highlight the efficacy of DRL-based approaches in dynamically adapting to the unique characteristics of HSR environments, achieving optimal resource allocation and maintaining high QoS. This paper contributes to advancing task partitioning strategies for HSR systems and lays the groundwork for future research in MEC-based HSR systems.