Content delivery networks (CDNs) are key components of high throughput, low latency services on the internet. CDN cache servers have limited storage and bandwidth and implement state-of-the-art cache admission and eviction algorithms to select the most popular and relevant content for the customers served. The aim of this study was to utilize state-of-the-art recommender system techniques for predicting ratings for cache content in CDN. Matrix factorization was used in predicting content popularity which is valuable information in content eviction and content admission algorithms run on CDN edge servers. A custom implemented matrix factorization class and MyMediaLite were utilized. The input CDN logs were received from a European telecommunication service provider. We built a matrix factorization model with that data and utilized grid search to tune its hyper-parameters. Experimental results indicate that there is promise about the proposed approaches and we showed that a low root mean square error value can be achieved on the real-life CDN log data.
翻译:内容传输网络(CDN)是互联网上高传输量、低潜值服务的关键组成部分。CDN缓存服务器的存储和带宽有限,并采用最新的缓存接收和驱逐算法,为客户选择最受欢迎和最相关的内容。这项研究的目的是利用最先进的推荐系统技术来预测CDN的缓存内容评级。矩阵因子化用于预测内容受欢迎程度,这是CDN边缘服务器上内容驱逐和内容接收算法中的宝贵信息。采用了定制的矩阵因子化分类和MyMediaLite。输入的CDN日志来自欧洲电信服务提供商。我们用这些数据建立了一个矩阵因子化模型,并利用网格搜索来调节其超参数。实验结果表明,对拟议的方法有希望,我们显示,在实时CDN日志数据上可以实现低根平均误差值。