Elastic Network Cache Control Using Deep Reinforcement Learning

• Main Authors: Cho, Chunglae, Shin, Seungjae, Jeon, Hongseok, Yoon, Seunghyun
• Format: Conference Proceeding
• Language: English
• Subjects: Cache storage; Costs; Deep learning; deep reinforcement learning; elastic caching; Heuristic algorithms; Information and communication technology; non-stationary traffic; Reinforcement learning; utility-minus-cost maximization; Virtualization
• ISSN: 2162-1241
• DOI: 10.1109/ICTC55196.2022.9952648

Thanks to the development of virtualization technology, content service providers can flexibly lease virtualized resources from infrastructure service providers when they deploy the cache nodes in edge networks. As a result, they have two orthogonal objectives: to maximize the caching utility on the one hand and minimize the cost of leasing the cache storage on the other hand. This paper presents a caching algorithm using deep reinforcement learning (DRL) that controls the caching policy with the content time-to-live (TTL) values and elastically adjusts the cache size according to a dynamically changing environment to maximize the utility-minus-cost objective. We show that, under non-stationary traffic scenarios, our DRL-based approach outperforms the conventional algorithms known to be optimal under stationary traffic scenarios.