Fair Scheduling Through Collaborative Filtering on Multicore Systems

Modern applications are being increasingly demanding in terms of computing capabilities, and high performance is required at all times. Chip multiprocessors (CMPs) comprise multiple cores and have been widely employed to address this demand. However, the cores of a CMP share several components of the memory hierarchy for which concurrent executing applications compete to access at run-time. This contention can lead to severe performance loss and has a different impact on each application, resulting in potential starvation for selected applications. Thus, there is a need for a scheduling policy to efficiently address this contention-induced unfairness. In this work, we utilize matrix reconstruction techniques to enhance scheduling decisions at run-time, ensuring the fair and efficient execution of any given application workload. Our evaluation shows that our proposed scheduling policy can achieve up to 25.8% gains in fairness when compared to the Linux completely fair scheduler, and up to 6.1% when compared to another state-of-the-art approach, without inflicting performance degradation.