Abstract:
Today’s computing has entered the cloud and AI era, where application workloads are highly diverse and dynamic, and the underlying hardware is increasingly heterogeneous. Operating systems remain the foundation that manages hardware and upon which all applications depend. However, achieving the best possible performance for each workload instance on a particular hardware setting remains difficult for two reasons. First, OS structure can prevent applications from fully exploiting hardware capabilities. Second, OS internals rely on static decisions that cannot fit all combinations of workloads and hardware.
The goal of my research is to enable optimal performance across all workload instances. To achieve this, I build instance-customizable operating systems—OSes that can be customized at runtime to hardware and workloads without breaking compatibility. In this talk, I present two ideas that advance this vision. First, I introduce file systems as processes, which customize OS structure to better deliver storage performance to applications. Next, I describe an approach that customizes OS internals: performance tuning decisions as programmable policies. This approach introduces principles for flexible, safe, and fast in-situ tuning of previously fixed, performance-critical constants in deployed kernels, enabling millisecond-scale updates and up to 50× performance improvements. Together, these ideas illustrate how operating systems can be made customizable per instance to deliver high performance across diverse workloads and hardware.
Biography:
Jing Liu is a Senior Researcher at Microsoft Research Asia. She received her Ph.D. from the University of Wisconsin–Madison in 2024. Her research includes datacenter-scale operating systems, file and storage systems, and distributed systems, with a focus on redesigning system mechanisms and interfaces so that machine learning–based techniques can meaningfully participate in system problems such as adaptation, control, and optimization. Jing is a recipient of the Erik Riedel Best Paper Award at FAST 2025 and a Meta Ph.D. Research Fellowship in Systems. She has served on the program committees of SOSP, OSDI, NSDI, and FAST, and is a Shadow PC Co-Chair for EuroSys 2026. More details can be found at https://jingliu.xyz.