Game Based Task Offloading in Cyber-Physical Systems for High-Speed Railway With Dynamic Latency and Energy Cost

The intelligentization of cyber-physical systems has led to an influx of computational tasks, placing substantial strain on the systems' computational and storage resources. High-speed railway (HSR), as a representative cyber-physical system, requires increased task completion rates and reduced energy consumption within defined timeframes. Effectively managing the surge in computational tasks within cyber-physical systems has become an urgent issue requiring resolution. This paper introduces a game-based task offloading strategy that addresses dynamic latency and energy consumption. Specifically, the study employs Stochastic Network Calculus (SNC) to capture the impact of transmission latency on system performance and obtain the bounds and probability distribution of transmission latency. Subsequently, the task offloading problem is formulated as a potential game, with each task acting as a player optimizing its objective, which includes the task completion rate and energy consumption. The Nash Equilibrium state is derived to ensure the existence of a task offloading strategy. Additionally, a reinforcement learning algorithm, Multi-Agent Deep Deterministic Policy Gradient (MADDPG), is proposed to achieve the Nash Equilibrium and optimize the task offloading strategy. Finally, extensive simulations demonstrate that the MADDPG algorithm outperforms other algorithms and exhibits fast convergence. Moreover, an appropriate violation probability derived from SNC can reduce system costs.