Linux内核中的机器学习（ML）库
Machine Learning (ML) library in Linux kernel
AI总结：
本文提出在Linux内核中引入机器学习库的架构，以解决配置复杂性和性能问题，并通过证明概念项目验证其可行性。
摘要：
Linux内核是一个庞大的代码库，包含大量的子系统和可能的配置选项，导致在制定高效配置时出现难以管理的复杂性。机器学习（ML）是一种通过数据学习、发现模式并做出预测的方法，无需开发人员实现算法，可以为Linux内核引入自我进化的能力。然而，在Linux内核中引入ML方法并不容易，因为内核空间中没有直接使用浮点运算（FPU），潜在地，ML模型可能会导致Linux内核的显著性能下降。本文提出了一种Linux内核中的ML基础设施架构，以解决上述问题并引入在内核空间中使用ML模型的方法。所建议的内核ML库已作为证明概念（PoC）项目实现，目标是演示建议的可行性，并设计内核空间ML模型代理与ML模型用户空间线程之间交互的接口。
英文摘要：
Linux kernel is a huge code base with enormous number of subsystems and possible configuration options that results in unmanageable complexity of elaborating an efficient configuration. Machine Learning (ML) is approach/area of learning from data, finding patterns, and making predictions without implementing algorithms by developers that can introduce a self-evolving capability in Linux kernel. However, introduction of ML approaches in Linux kernel is not easy way because there is no direct use of floating-point operations (FPU) in kernel space and, potentially, ML models can be a reason of significant performance degradation in Linux kernel. Paper suggests the ML infrastructure architecture in Linux kernel that can solve the declared problem and introduce of employing ML models in kernel space. Suggested approach of kernel ML library has been implemented as Proof Of Concept (PoC) project with the goal to demonstrate feasibility of the suggestion and to design the interface of interaction the kernel-space ML model proxy and the ML model user-space thread.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
