ETRI-Knowledge Sharing Plaform



논문 검색
구분 SCI
연도 ~ 키워드


학술대회 Elastic Network Cache Control Using Deep Reinforcement Learning
Cited 0 time in scopus Download 2 time Share share facebook twitter linkedin kakaostory
조충래, 신승재, 전홍석, 윤승현
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1006-1008
22HH8400, 주문형 데이터 기반 네트워크 지능화 프레임워크 기술 개발, 윤승현
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.
KSP 제안 키워드
Based Approach, Cache Nodes, Cache size, Cache storage, Caching Policy, Changing environment, Deep reinforcement learning, Edge network, Elastic network, Non-stationary Traffic, Reinforcement Learning(RL)