用Mamba模型提升边缘缓存热门内容预测精度，降低延迟。
CacheMamba: Popularity Prediction for Mobile Edge Caching Networks via Selective State Spaces
- 基于Mamba状态空间模型分析请求时序数据，预测热门文件
- 在长序列下缓存命中率、MAP、NDCG均优于Transformer
- 适合高实时性需求的AR/VR、自动驾驶等场景
移动边缘缓存（MEC）通过动态将高频请求内容缓存在边缘服务器上，在降低数据密集型服务延迟方面起关键作用，对增强现实（AR）、虚拟现实（VR）和自动驾驶（AV）等应用尤为重要。本文针对MEC中的热点预测问题，利用历史请求时间序列数据，将其建模为排序任务。提出CacheMamba模型，采用Mamba这一基于状态空间模型（SSM）的架构，识别最可能被请求的前K个文件。与基于Transformer的方法对比，CacheMamba在缓存命中率、平均精度（MAP）、归一化折损累计增益（NDCG）及每秒浮点运算量（FLOPS）方面表现更优，尤其在处理长序列时优势显著。
原文摘要 · Abstract (English)
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.
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