Abstract:

In this thesis we propose using the scientific method to develop a deeper understanding of CPU schedulers; we use this approach to explain and understand the sometimes erratic behavior of CPU schedulers. This approach begins with introducing controlled workloads into commodity operating systems and observing the CPU scheduler's behavior. From these observations we are able to infer the underlying CPU scheduling policy and create models that predict scheduling behavior.

We have made two advances in the area of applying scientific analysis to CPU schedulers. The first, CPU Futures, is a combination of predictive scheduling models embedded into the CPU scheduler and user-space controller that steers applications using feedback from these models. We have developed these predictive models for two different Linux schedulers (CFS and O(1)), based on two different scheduling paradigms (timesharing and proportional-share). Using three different case studies, we demonstrate that applications can use our predictive models to reduce interference from low-importance applications by over 70%, reduce web server starvation by an order of magnitude, and enforce scheduling policies that contradict the CPU scheduler's.

Harmony, our second contribution, is a framework and set of experiments for extracting multiprocessor scheduling policy from commodity operating systems. We used this tool to extract and analyze the policies of three Linux schedulers: O(1), CFS, and BFS. These schedulers often implement strikingly different policies. At the high level, the O(1) scheduler carefully selects processes for migration and strongly values processor affinity. In contrast, CFS continuously searches for a better balance and, as a result, selects processes for migration at random. BFS strongly values fairness and often disregards processor affinity.

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