A data-driven approach of performance evaluation for cache server groups in content delivery network

In industry, Content Delivery Network (CDN) service providers are increasingly using data-driven mechanisms to build the performance models of the service-providing systems. Building a model to accurately describe the performance of the existing infrastructure is very crucial to make resource management decisions. Conventional approaches that use hand-tuned parameters or linear models have their drawbacks. Recently, data-driven paradigm has been shown to greatly outperform traditional methods in modeling complex systems. We design a data-driven approach to building a reasonable and feasible performance model for CDN cache server groups. We use deep LSTM auto-encoder to capture the temporal structures from the high-dimensional monitoring data, and use a deep neural network to predict the reach rate which is a client QoS measurement from the CDN service providers' perspective. The experimental results have shown that our model is able to outperform state-of-the-art models.