CacheMamba: Popularity Prediction for Mobile Edge Caching Networks via Selective State Spaces

TL;DR

CacheMamba introduces a novel state-space model for predicting popular files in mobile edge caching, outperforming Transformer-based methods in accuracy and efficiency for latency-sensitive applications.

Contribution

The paper presents CacheMamba, a new SSM-based approach for popularity prediction in MEC, demonstrating superior performance over Transformer models especially with longer request sequences.

Findings

CacheMamba achieves higher cache-hit rates.

It outperforms Transformer models in MAP and NDCG.

It is more efficient in terms of FLOPS for longer sequences.

Abstract

Mobile Edge Caching (MEC) plays a pivotal role in mitigating latency in data-intensive services by dynamically caching frequently requested content on edge servers. This capability is critical for applications such as Augmented Reality (AR), Virtual Reality (VR), and Autonomous Vehicles (AV), where efficient content caching and accurate popularity prediction are essential for optimizing performance. In this paper, we explore the problem of popularity prediction in MEC by utilizing historical time-series request data of intended files, formulating this problem as a ranking task. To this aim, we propose CacheMamba model by employing Mamba, a state-space model (SSM)-based architecture, to identify the top-K files with the highest likelihood of being requested. We then benchmark the proposed model against a Transformer-based approach, demonstrating its superior performance in terms of cache-hit rate, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), and Floating-Point Operations Per Second (FLOPS), particularly when dealing with longer sequences.