Contenders: Predicting Cache Contention of Co-Scheduled Applications
Ze Wang, Tim Süß, André Brinkmann, Lars Nagel · 2025
To increase the resource utilisation rate in modern data centres, multiple applications are co-scheduled on one server, which can lead to severe performance degradation due to the contention for shared resources. This makes it desirable to accurately predict the performance impact before making a scheduling decision. This work considers contention in the last-level cache (LLC), a scarce resource that must be shared by co-scheduled applications. We propose Contenders, a novel approach to predict the increase in cache misses and running time that applications would experience if they were co-scheduled. It generates a frequencylayered profile for each application by running it with a new set of micro-benchmarks. Based on the profiles, it estimates how much of the contested cache each application would occupy and derives its cache misses and running time from it. Our approach is evaluated on selected and random sets of applications. It is compared with two state-of-the-art prediction tools, and the results demonstrate that Contenders has a much higher prediction accuracy than these tools.