用强化学习动态调频，让边缘设备在多任务下省电3%-10%。
Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing
- 用系统利用率编码时序数据，指导强化学习生成节能调度策略。
- 在Jetson Nano上实测，相比默认调度器最多省电10%。
- 适合需要低功耗运行的多任务边缘计算场景。
在边缘设备上为多任务软实时应用寻找适应性强、节能高效的动态电压频率调节（DVFS）策略始终面临挑战。本文研究具有多任务、多截止时间的通用系统，采用基于强化学习的DVFS方法实现能效优化。针对以往工作将周期性系统简化为单任务单截止时间的不足，本研究提出两种仅依赖系统利用率性能计数器的时序数据编码方法，仅需内核少量用户空间信息即可完成特征提取。方法在Jetson Nano（2GB）平台实现，测试了三类固定多任务负载（分别含3、5、8个任务），并通过随机负载生成器构建多样化任务组合以验证泛化能力。实验表明，该方法相比Linux内置调度器可节省3%-10%能耗。
原文摘要 · Abstract (English)
Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving for periodic soft real-time applications on edge devices. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplified to cope with complex situations. The method encodes time series data in the Linux kernel into information that is easy to interpret for reinforcement learning, allowing the system to generate DVFS policies to adapt system patterns based on the general workload. For encoding, we present two different methods for comparison. Both methods use only one performance counter: system utilization, and the kernel only needs minimal information from the userspace. Our method is implemented on Jetson Nano Board (2GB) and is tested with three fixed multitask workloads, which are three, five, and eight tasks in the workload, respectively. For randomness and generalization, we also designed a random workload generator to build different multitask workloads to test. Based on the test results, our method could save 3%-10% power compared to Linux built-in governors.
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