Abstract

Existing cache replacement strategies in large-scale spatiotemporal data systems struggle to cope with complex and dynamic access patterns characterized by long-tail distributions and periodic behaviors. Traditional heuristic-based methods such as Least Recently Used (LRU) and Least Frequently Used (LFU) frequently fail to generalize across varying workloads, while recent learning-based approaches are limited by their reliance on hand-crafted features or short-term dependencies. In this paper, we propose a cache replacement framework named CorrelaCache, which integrates imitation learning with a temporal autocorrelation mechanism to capture both short-term and long-range periodic access patterns. By modeling the replacement task as a Markov Decision Process (MDP) and using the Belady optimal policy as the supervision signal, our method adopts Long Short-Term Memory (LSTM) networks for sequential encoding and employs Fast Fourier Transform (FFT)-based autocorrelation to detect and align periodic phases in access history. We further incorporate a joint prediction layer and a hybrid loss function that combines ranking loss and reuse distance prediction loss, and mitigate distributional shift during training via the Dataset Aggregation (DAgger) algorithm. Experimental results on five public meteorological datasets with generated hydrological access traces show that CorrelaCache outperforms representative baselines in the evaluated workloads.

IPC Classification

Keywords