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As ubiquitous and personalized services are growing boomingly, anincreasingly large amount of traffic is generated over the network by massivemobile devices. As a result, content caching is gradually extending to networkedges to provide low-latency services, improve quality of service, and reduceredundant data traffic. Compared to the conventional content delivery networks,caches in edge networks with smaller sizes usually have to accommodate morebursty requests. In this paper, we propose an evolving learning-based contentcaching policy, named PA-Cache in edge networks. It adaptively learnstime-varying content popularity and determines which contents should bereplaced when the cache is full. Unlike conventional deep neural networks(DNNs), which learn a fine-tuned but possibly outdated or biased predictionmodel using the entire training dataset with high computational complexity,PA-Cache weighs a large set of content features and trains the multi-layerrecurrent neural network from shallow to deeper when more requests arrive overtime. We extensively evaluate the performance of our proposed PA-Cache onreal-world traces from a large online video-on-demand service provider. \rb{Theresults show that PA-Cache outperforms existing popular caching algorithms andapproximates the optimal algorithm with only a 3.8\% performance gap when thecache percentage is 1.0\%}. PA-Cache also significantly reduces thecomputational cost compared to conventional DNN-based approaches.

PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks