Advanced Topics in Operating Systems

Advanced Topics in Operating Systems

This course explores current research and engineering challenges at the intersection of operating systems, storage, and distributed computing. It is structured in two parts.

The first part consists of six lectures on selected topics, including systems benchmarking and performance evaluation, ad-hoc file systems, distributed locking, and caching strategies. Some of these sessions are held in an inverted-classroom format: students prepare the material in advance using provided readings and recordings, and class time is devoted to discussion, problem solving, and deeper analysis. This part concludes with a mid-term exam. Passing the mid-term exam is a prerequisite for admission to the second part of the course.

The second part is strongly hands-on. Working in small teams, students apply modern machine learning methods to an existing ad-hoc file system: first to analyze its behavior and performance characteristics based on traces, metrics, and workload data, and subsequently to optimize it by developing and evaluating data-driven improvements, e.g., in caching, prefetching, or data placement. To support the development work, participants in the project phase receive a license for Claude Code, an agentic AI coding assistant, for the duration of the course. The project phase concludes with a presentation and a written report in which students document their approach, experimental setup, and results.

By the end of the course, students will be able to design and conduct rigorous performance evaluations of systems software, understand the design space of ad-hoc file systems and distributed coordination mechanisms, and apply ML techniques to real-world systems optimization problems.

Prerequisites: a solid background in operating systems and systems programming (C/C++ or Rust); basic familiarity with Python and machine learning is helpful but not required.