Title
Neighboring-Aware Caching in Heterogeneous Edge Networks by Actor-Attention-Critic Learning
Abstract
With the development of network technology and the surge in demand, the speed and throughput of data and applications are leading to the skyrocketing increase in traffic. The communication and collaboration between heterogeneous edge servers are indispensable. In this scenario with heterogeneous edges, there is a common understanding on the fact that an effective edge caching algorithm could play the role of enabler to reduce the network resource consumption and content fetch delay. However, most of the existing studies on multi-agent caching methods focus more on the overall situation, while ignoring the mutual influence between different agents. In this context, we model the edge caching content replacement problem as a Markov process and deploy attention mechanism based on the Actor-Attention-Critic algorithm to realize a neighboring-aware edge caching (NAEC) strategy. The proposed method makes full use of the communication between base stations to exchange neighboring information, so that we can reduce the pressure on the backbone and further improve user satisfaction. The simulation results have verified the feasibility and effectiveness of the proposed algorithm.
Year
DOI
Venue
2021
10.1109/ICC42927.2021.9500929
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
Keywords
DocType
ISSN
heterogeneous network, content caching, attention mechanism, multi-agent
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
11
Authors
5
Name
Order
Citations
PageRank
Yiwei Zhao112.06
Ruibin Li261.76
Chenyang Wang3816.10
Xiaofei Wang468658.88
Victor C. M. Leung59717759.02