用强化学习动态调整任务复制数，兼顾可靠性和能效。
RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems
- 根据实时系统状态动态决定任务复制数量。
- 功耗降低63%，调度成功率提升53%。
- 适合对能效与可靠性要求高的嵌入式系统。
嵌入式系统支撑众多现代应用，需满足严格的可靠性、实时性、热管理和功耗要求。任务复制可通过重复执行来应对瞬态和永久故障，但盲目复制常导致过度开销和高温。现有设计期方法通常基于最坏情况确定副本数，正常运行时造成资源浪费。本文提出RL-TIME，一种基于强化学习的方法，根据实际系统状态动态决策副本数。通过在运行时同时考虑可靠性目标和核心级热安全功耗（TSP）约束，RL-TIME避免了不必要的开销与过热。实验表明，相比现有最优方法，RL-TIME将功耗降低63%，调度成功率提高53%，且在72%的情况下满足TSP约束。
原文摘要 · Abstract (English)
Embedded systems power many modern applications and must often meet strict reliability, real-time, thermal, and power requirements. Task replication can improve reliability by duplicating a task's execution to handle transient and permanent faults, but blindly applying replication often leads to excessive overhead and higher temperatures. Existing design-time methods typically choose the number of replicas based on worst-case conditions, which can waste resources under normal operation. In this paper, we present RL-TIME, a reinforcement learning-based approach that dynamically decides the number of replicas according to actual system conditions. By considering both the reliability target and a core-level Thermal Safe Power (TSP) constraint at run-time, RL-TIME adapts the replication strategy to avoid unnecessary overhead and overheating. Experimental results show that, compared to state-of-the-art methods, RL-TIME reduces power consumption by 63%, increases schedulability by 53%, and respects TSP 72% more often.
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