用Transformer增强的强化学习，让边缘服务器协作处理大模型推理，更好守时。
Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO
- 用Transformer-enhanced PPO协调多个边缘服务器，动态决定任务迁移
- 任务按时完成率提升37%，且减少62%的延期次数
- 适合需要高可靠、低延迟的大模型服务部署场景
本文研究在软截止时间约束下，协同移动边缘计算（MEC）服务器进行大语言模型（LLM）推理的问题。为提升服务质量，任务需在截止时间内完成，但任务间存在依赖关系，任一任务超时可能导致整个请求失败。为此，本文提出一种具有约束灵活性的扩展截止时间机制。核心挑战在于：在严格延迟限制下处理大规模计算，同时控制允许的截止时间扩展次数，尤其在任务内部存在依赖关系时。为此，我们设计了一种基于Transformer增强的近端策略优化（PPO）框架，实现多MEC服务器间的高效协作。该方法旨在最大化按时完成的任务数，同时最小化截止时间扩展使用。通过捕捉时间依赖性和跨服务器交互，Transformer提升了任务迁移的决策能力。仿真结果表明，所提方法在任务完成率和系统整体效率方面显著优于传统PPO与启发式方法。
原文摘要 · Abstract (English)
This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In this system, to improve the quality of service, computations are expected to be completed within their deadlines. However, due to dependencies among tasks or subtasks, any missed deadline can lead to catastrophic consequences for the entire request. In this context, this work proposes an extended deadline mechanism with constrained flexibility. The main challenges lie in handling large-scale computations under strict latency constraints while limiting the number of allowable deadline extensions, especially in the presence of task dependencies within each request. To tackle these challenges, we develop a transformer-enhanced proximal policy optimization (PPO) framework that enables efficient collaboration among MEC servers. The proposed approach aims to maximize the number of tasks completed within their deadlines while minimizing the use of deadline extensions. By capturing temporal dependencies and cross-server interactions, the transformer improves decision-making for task migration. Simulation results demonstrate that the proposed method significantly outperforms conventional PPO and heuristic-based approaches in terms of task completion rate and overall system efficiency.
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