GNN-GM: A Proactive Caching Scheme for Named Data Networking
2022 IEEE International Conference on Communications Workshops (ICC Workshops)(2022)
摘要
As people spend more time watching movies and sharing videos online, it is crucial to provide users with a satisfactory quality of experience (QoE). With the help of the in-network caching feature in named data networking (NDN), our paper aims to improve user experience through caching. We propose a graph neural network-gain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users’ ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of caching the video. Third, we propose a cache placement algorithm to maximize the caching gains and proactively cache videos. We also design a caching replacement strategy based on the gain of caching the video. We utilize a real-world dataset to evaluate our caching strategy. Compared to state-of-the-art caching approaches, experimental results show that our caching policy improves cache hit rate by 25%, reduces latency by 5%, and reduces server load by 7%.
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关键词
named data networking,deep learning,predict rating,caching decision,content placement,proactive caching
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