Predictive Caching@Scale

Increasing content and services available on the Internet has lead to substantial growth in network traffic. A large distributed caching platform facilitate low-latency and high throughput for web and video content over the public Internet. However, the edge-infrastructure cannot grow at the rate of traffic and maintain quality service at low cost. With increasing long-tail content footprints and performance sensitive users, content-agnostic caching schemes fail to evolve with changing traffic popularity profiles which leads to poor caching decisions. At Akamai we built a very high performant, cost sensitive, content aware caching system that uses Machine Learning, to run on our distributed delivery platform. The developed ML-based caching algorithm, Prediction Error-Correcting Caching (PeCC), is cost competitive with a classical algorithm like LRU even when deployed on commodity hardware while achieving cache-hit ratios close to theoretically optimal caching schemes. We will talk about the main challenges and details in deploying PeCC. Introduction to web-traffic and how to build Deep Neural Networks based caching to scale cost-effectively. Second, deployment of a very compute intensive DNN models alongside a real-time web proxy with very tight performance guarantees. We will also discuss some key takeaways from deploying ML for system scalability.