DEAP Cache: Deep Eviction Admission and Prefetching for Cache

TL;DR

DEAP Cache introduces an end-to-end machine learning framework that simultaneously learns prefetching, admission, and eviction policies for caching, leveraging deep networks and online modeling to adapt to non-stationary data.

Contribution

It is the first to jointly learn all three cache policies using deep learning and reinforcement learning, incorporating future trend modeling and online data distribution estimation.

Findings

Effective modeling of future frequency and recency improves cache performance.

Online Kernel Density Estimation handles non-stationary data distributions.

Joint learning of cache policies demonstrates promising results as a proof of concept.

Abstract

Recent approaches for learning policies to improve caching, target just one out of the prefetching, admission and eviction processes. In contrast, we propose an end to end pipeline to learn all three policies using machine learning. We also take inspiration from the success of pretraining on large corpora to learn specialized embeddings for the task. We model prefetching as a sequence prediction task based on past misses. Following previous works suggesting that frequency and recency are the two orthogonal fundamental attributes for caching, we use an online reinforcement learning technique to learn the optimal policy distribution between two orthogonal eviction strategies based on them. While previous approaches used the past as an indicator of the future, we instead explicitly model the future frequency and recency in a multi-task fashion with prefetching, leveraging the abilities of deep networks to capture futuristic trends and use them for learning eviction and admission. We also model the distribution of the data in an online fashion using Kernel Density Estimation in our approach, to deal with the problem of caching non-stationary data. We present our approach as a "proof of concept" of learning all three components of cache strategies using machine learning and leave improving practical deployment for future work.