CacheMamba: Popularity Prediction for Mobile Edge Caching Networks via Selective State Spaces
This addresses latency reduction in data-intensive services like AR, VR, and AV by improving caching efficiency, though it is incremental as it applies a known method to a specific domain.
The paper tackles the problem of predicting content popularity for mobile edge caching networks using historical time-series request data, proposing CacheMamba, a model based on state-space architectures, which outperforms Transformer-based approaches in cache-hit rate, MAP, NDCG, and FLOPS, especially for longer sequences.
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.