Recommendation systems for suggesting items to users are commonplace in online services such as marketplaces, content delivery platforms and ad placement systems. Such systems, over time, learn from user feedback, and improve their recommendations. An important caveat, however, is that both the distribution of user types and their respective preferences change over time, thus inducing changes in the optimal recommendation and requiring the system to periodically "reset" its learning. We consider systems with known change-points (aka episodes) in the distribution of user-features and preferences. Examples include seasonality in product recommendations where there are marked changes in interests based on time-of-year, or ad-placements based on time-of-day. While a baseline strategy would be to re-learn the recommendation algorithm in each episode, it is often advantageous to share some learning across episodes. Specifically, one often has access to (potentially, a very) large number of pre-trained recommendation algorithms (aka experts), and the goal then is to quickly determine (in an online manner) which expert is best suited to a specific episode.

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.