Journal
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Volume 20, Issue 1, Pages 246-260Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2022.3198074
Keywords
Logic gates; Servers; Cooperative caching; Computational modeling; Training; Databases; Feature extraction; edge caching; deep reinforcement learning
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In this paper, an efficient cooperative caching (FDDL) framework is proposed to address key issues in mobile edge networks. Machine learning techniques are used to improve content placement and reduce computation complexity and communication costs. The framework includes a cache admission algorithm, a lightweight eviction algorithm for fine-grained replacements, and a federated learning-based parameter sharing mechanism. Experimental results show that the proposed FDDL achieves higher cache hit ratio and traffic offloading rate, and effectively reduces communication costs and training time.
Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.
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