Latency-sensitive heuristic task offloading method in edge computing

Abstract:

In order to address the challenge of designing reasonable offloading decisions for mobile edge computing (MEC) in multi-user environments, which leads to load imbalance, excessive total latency, and response delays, this paper proposed a latency-sensitive heuristic task offloading method. Firstly, to address the issues of limited computational resources and insufficient battery power of edge devices during computation task processing, the paper introduced an edge server-centric offloading paradigm and established a system model and a latency optimization model. Subsequently, it introduced an improved proximal policy optimization algorithm (I-PPO), which extended the offline training process, designed a reward mechanism that considers the impact of multi-agent decisions, and incorporated global information based on specific agents into the features, enabling the algorithm to be suitable for multi-user environments. Furthermore, building upon I-PPO, the paper introduced task priority scheduling decisions into the task offloading execution process, resulting in the development of a latency-sensitive lightweight heuristic task offloading algorithm, denoted as HTAI. This further optimized system latency and enhanced user satisfaction. Simulation experiments demonstrate that the I-PPO algorithm proposed in this paper, compared to similar algorithms, effectively improves convergence speed, optimization capability, and robustness, and it can be applied in multi-agent environments. Moreover, the algorithm proposed herein outperforms other algorithms in terms of total system latency and edge server load balance, exhibiting strong stability.