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i-CU: Intelligent Cache Replacement and Content Update for Data Freshness in Cloud-Edge Networks
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- Authors
- Sinwoong Yun, Dongsun Kim, Sungjin Lee, Jemin Lee
- Issue Date
- 2025-12
- Citation
- IEEE Transactions on Mobile Computing, v.24, no.12, pp.12742-12755
- ISSN
- 1536-1233
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- As the demand on time-sensitive contents increases, data freshness recently becomes an important performance metric in the cache-enabled networks. Therefore, in this paper, we design the joint cache replacement and content update algorithm in the cloud-edge networks considering the data freshness at both the cloud server and the edge server. We define a fresh content acquisition with cache hit (FACH) ratio as a performance metric, which shows the portion of users obtaining the requesting content from the edge server while satisfying the freshness constraint. To maximize the FACH ratio, we propose the reinforcement learning (RL)-based algorithm, named the intelligent Cache replacement and content Update(i-CU) algorithm. In the proposed algorithm, we newly suggest the score-based action decision to reduce the action space while guaranteeing the constraints of the problem. In the simulation results, we develop and evaluate the i-CU algorithm for various datasets, which verifies that the i-CU algorithm can achieve the higher FACH ratio compared to the existing baselines under the various network parameters.
- KSP Keywords
- Action space, Cache Replacement, Cloud server, Edge Networks, Reinforcement learning(RL), Time-sensitive, Update algorithm, cache hit, data freshness, network parameters, performance metrics