一句话：构建 LAKE 系统，为内核空间引入 ML 决策与加速器支持，并验证可显著降低推理开销。
现代操作系统内核日益复杂、硬件快速多样化且机器学习持续演进，作者探索用 ML 改进内核决策。论文在内存管理、进程与 I/O 调度等五个依赖手工启发式的子系统中尝试 ML 驱动决策，并分析其对内核设计、共享组件和硬件加速访问的影响。研究发现，GPU 等专用硬件对承担 ML 额外负载至关重要，但内核空间加速器可访问性不足；收益依赖子系统、工作负载和硬件。为此作者构建 LAKE，提供跨抽象层与模块边界的特征收集管理 API、管理加速收益波动的机制，以及缓解用户态与内核态资源竞争的接口。评估显示，ML 支持的 I/O 延迟预测器在加速后推理时间最多降低 96%。
The complexity of modern operating systems (OSes), rapid diversification of hardware, and steady evolution of machine learning (ML) motivate us to explore the potential of ML to improve decision-making in OS kernels. We conjecture that ML can better manage tradeoff spaces for subsystems such as memory management and process and I/O scheduling that currently rely on hand-tuned heuristics to provide reasonable average-case performance. We explore the replacement of heuristics with ML-driven decision-making in five kernel subsystems, consider the implications for kernel design, shared OS-level components, and access to hardware acceleration. We identify obstacles, address challenges and characterize tradeoffs for the benefits ML can provide that arise in kernel-space. We find that use of specialized hardware such as GPUs is critical to absorbing the additional computational load required by ML decisioning, but that poor accessibility of accelerators in kernel space is a barrier to adoption. We also find that the benefits of ML and acceleration for OSes is subsystem-, workload- and hardware-dependent, suggesting that using ML in kernels will require frameworks to help kernel developers navigate new tradeoff spaces. We address these challenge by building a system called LAKE for supporting ML and exposing accelerators in kernel space. LAKE includes APIs for feature collection and management across abstraction layers and module boundaries. LAKE provides mechanisms for managing the variable profitability of acceleration, and interfaces for mitigating contention for resources between user and kernel space. We show that an ML-backed I/O latency predictor can have its inference time reduced by up to 96% with acceleration.
@inproceedings{paperbot10039,
title = {Towards a Machine Learning-Assisted Kernel with LAKE},
author = {Henrique Fingler and Isha Tarte and Hangchen Yu and Ariel Szekely and Bodun Hu and Aditya Akella and Christopher J. Rossbach},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3575693.3575697}
}