The problem with that approach is not only the time and effort to capture and analyze the trace, but that the workload changes over the application's lifetime.

The problem with that approach is not only the time and effort to capture and analyze the trace, but that the workload changes over the application's lifetime. When the workload is expensive and important then cache analysis is worth doing, and there are cache simulators that are pretty easy to work with. For the general case the ideal is an eviction policy that adapts to fit the given workload. Unfortunately adaptivity is not a very well explored area so most policies have heuristic settings that default to work well in a target range of workloads, but this has to be tuned for others. In this and the author's other paper, S3-FIFO, their lower hit rates with TinyLFU turned out to be due to the implementation. When running it with Caffeine, a productionized version that adds hill climbing and jitter, it had equivalent hit rates [1]. That cache is pretty robust across a wide range of workloads, but also much more complicated to implement. I'd really like to see more research on adaptivity because I think all policies are converging on similar structures and goals, but self-tuning is still an error prone art. [1] https://github.com/Yiling-J/theine/issues/21#issuecomment-1880217400 https://github.com/Yiling-J/theine/issues/21#issuecomment-18...