SwiftCache: Model-Based Learning for Dynamic Content Caching in CDNs
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要