2024/02/27 by Bahman Abolhassani, Abolhassani, Bahman, Atilla Eryılmaz +3 · 1 citation
Computer Science · Engineering · #Caching and Content Delivery #Cooperative Communication and Network Coding #FOS: Mathematics #IPv6, Mobility, Handover, Networks, Security #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2402.17111
openalex publication_date 2024/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce SwiftCache, a "fresh" learning-based caching framework designed for content distribution networks (CDNs) featuring distributed front-end local caches and a dynamic back-end database. Users prefer the most recent version of the dynamically updated content, while the local caches lack knowledge of item popularity and refresh rates. We first explore scenarios with requests arriving at a local cache following a Poisson process, whereby we prove that the optimal policy features a threshold-based structure with updates occurring solely at request arrivals. Leveraging these findings, SwiftCache is proposed as a model-based learning framework for dynamic content caching. The simulation demonstrates near-optimal cost for Poisson process arrivals and strong performance with limited cache sizes. For more general environments, we present a model-free Reinforcement Learning (RL) based caching policy without prior statistical assumptions. The model-based policy performs well compared to the model-free policy when the variance of interarrival times remains moderate. However, as the variance increases, RL slightly outperforms model-based learning at the cost of longer training times, and higher computational resource consumption. Model-based learning's adaptability to environmental changes without retraining positions it as a practical choice for dynamic network environments. Distributed edge caches can utilize this approach in a decentralized manner to effectively meet the evolving behaviors of users.