LDOS: Toward a Learning-Directed Operating System (www.sigops.org)

The Learning Directed Operating System (LDOS) initiative aims to revolutionize operating systems (OS) by integrating machine learning to create adaptive, self-adjusting resource management policies. Despite significant advances in hardware and applications, traditional OS architectures remain largely unchanged since the 1990s, resulting in rigid policies that fail to accommodate the diverse and dynamic demands of modern applications. LDOS seeks to address this shortcoming by employing learned policies that adapt to workloads and environments, thereby optimizing resource sharing without the need for extensive manual configuration or overprovisioning. This project highlights the critical need for a paradigm shift in OS design, moving away from static heuristics toward a system where machine learning-driven policies can coordinate and adapt quickly. Key challenges include data dependencies for training effective models, the implications of potential decision-making errors, and performance overheads associated with these learned policies. The LDOS team's vision includes collecting rich telemetry data and using synthetic data generation to train models, all while ensuring smooth deployment and continuous adaptation without disrupting system operations. Ultimately, LDOS promises to enhance performance across varied domains like cloud computing and robotics, responding intelligently to evolving technological landscapes.