This paper lays down the research agenda for a domain-specific foundation
model for operating systems (OSes). Our case for a foundation model revolves
around the observations that several OS components such as CPU, memory, and
network subsystems are interrelated and that OS traces offer the ideal dataset
for a foundation model to grasp the intricacies of diverse OS components and
their behavior in varying environments and workloads. We discuss a wide range
of possibilities that then arise, from employing foundation models as policy
agents to utilizing them as generators and predictors to assist traditional OS
control algorithms. Our hope is that this paper spurs further research into OS
foundation models and creating the next generation of operating systems for the
evolving computing landscape.

本文研究了面向操作系统（OS）的特定领域基础模型的研究议程。我们提出了基础模型的案例，围绕着操作系统的多个组件（如 CPU、内存和网络子系统）相互关联的观察，并且利用操作系统的追踪数据集作为基础模型理解不同环境和工作负载下不同操作系统组件行为的理想数据集。我们讨论了一系列可能性，包括将基础模型作为策略代理、生成器和预测器来辅助传统操作系统控制算法等。我们希望本文能够推动对操作系统基础模型的进一步研究，并为不断演变的计算环境创造下一代操作系统。

关于操作系统的基础模型

On a Foundation Model for Operating Systems

Next Generation (NextG) cellular networks will be natively cloud-based and
built upon programmable, virtualized, and disaggregated architectures. The
separation of control functions from the hardware fabric and the introduction
of standardized control interfaces will enable the definition of custom
closed-control loops, which will ultimately enable embedded intelligence and
real-time analytics, thus effectively realizing the vision of autonomous and
self-optimizing networks. This article explores the disaggregated network
architecture proposed by the O-RAN Alliance as a key enabler of NextG networks.
Within this architectural context, we discuss the potential, the challenges,
and the limitations of data-driven optimization approaches to network control
over different timescales. We also present the first large-scale integration of
O-RAN-compliant software components with an open-source full-stack softwarized
cellular network. Experiments conducted on Colosseum, the world's largest
wireless network emulator, demonstrate closed-loop integration of real-time
analytics and control through deep reinforcement learning agents. We also show
the feasibility of Radio Access Network (RAN) control through xApps running on
the near real-time RAN Intelligent Controller, to optimize the scheduling
policies of co-existing network slices, leveraging the O-RAN open interfaces to
collect data at the edge of the network.

本文探讨了 O-RAN 联盟提出的解聚的网络架构是否为 NextG 网络的关键要素，并在此框架下研究了数据驱动优化方法的潜力、挑战和局限性，以及实现自主和自优化网络愿景的深度强化学习智能体通过实时分析与控制的闭环集成的可行性。