Music belongs to one of the most popular content categories overall, and it is nowadays mainly consumed using online streaming services. With YouTube being the largest source of traffic in most networks about half of all YouTube requests address music videos. To cope with the continuously growing demand for content and thus increasing network traffic, YouTube operates its own CDN, a globally distributed network of caches. This allows serving content from locations close to the users, which circumvents potential network bottlenecks and increases the user-perceived QoE due to reduced latency. Recently, proactive caching and prefetching has shown superior performance results compared with traditional reactive caching schemes such as LRU and LFU. Due to the substantial footprint of music videos on today's Internet, we propose a novel proactive caching strategy specifically for music videos. This strategy incorporates two key observations: i) Music genre and mood popularity varies over the course of the day and ii) A video's past views are predictive for its future popularity development. For the classification task, we use a Convolutional Neural Network while investigating several predictive models for the popularity estimation. The proposed caching system can increase the cache hit rate up to 4.5% which is substantial for caching systems.
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