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In the realm of operating systems, the heart of performance lies in the CPU scheduler: a critical component responsible for managing the execution of tasks on a system. Playing around with CPU scheduling policies has always been a dream for many kernel hackers and OS enthusiasts. However, such material typically remains within the domain of a few core kernel developers with extensive years of experience. What if we could bring this dream to life and make it really accessible, thanks to sched-ext
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Can we use machine learning to assist one of the most critical components of an operating system: the CPU scheduler? In this talk, we explore how Rust and AI can be combined together to experiment with intelligent scheduling decisions in the Linux kernel. LEVEL: Advanced
In the realm of operating systems, the heart of performance lies in the CPU scheduler: a critical component responsible for managing the execution of tasks on a system. Exploring and customizing CPU scheduling policies has long been the realm of few highly specialized kernel developers, leaving most developers and researchers without the tools to tailor system performance to their unique needs. What if we could unlock this potential and make it accessible to a wider audience, from game developers, cloud service providers to AI researchers and performance engineers? With sched-ext [1], eBPF and Rust, this vision can become a reality, enabling the development of multiple dynamic specialized schedulers, tailored to specific system workloads and architectural topologies, that can be loaded and managed as regular user-space programs [2]. [1] https://sched-ext.com/ [2] https://github.com/sched-ext/scx/tree/main/rust/scx_rustland_core
