상세정보
학술대회
Elastic Network Cache Control Using Deep Reinforcement Learning
- 발행일
- 202210
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1006-1008
- 협약과제
- 22HH8400, 주문형 데이터 기반 네트워크 지능화 프레임워크 기술 개발, 윤승현
- 초록
- 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)