Optimistic Online Caching for Batched Requests

TL;DR

This paper investigates online caching strategies that leverage machine learning predictions in a batched setting, demonstrating improved performance over traditional policies through experimental validation.

Contribution

It introduces and analyzes optimistic batched caching policies that reduce computational costs while enhancing cache efficiency compared to classic methods.

Findings

Batched optimistic policies outperform classical caching algorithms on real and stationary traces.

Batching reduces computational overhead in online caching with predictions.

Experimental results validate the effectiveness of batched optimistic approaches.

Abstract

In this paper we study online caching problems where predictions of future requests, e.g., provided by a machine learning model, are available. Typical online optimistic policies are based on the Follow-The-Regularized-Leader algorithm and have higher computational cost than classic ones like LFU, LRU, as each update of the cache state requires to solve a constrained optimization problem. In this work we analysed the behaviour of two different optimistic policies in a \textit{batched} case, i.e., when the cache is updated less frequently in order to amortize the update cost over time or over multiple requests. Experimental results show that such an optimistic batched approach outperforms classical caching policies both on stationary and real traces