SNN-cache: A practical machine learning-based caching system utilizing the inter-relationships of requests

Youngbin Im, Prasanth Prahladan, Tae Hwan Kim, Yong Geun Hong, Sangtae Ha

Abstract: An efficient caching algorithm needs to exploit the inter-relationships among requests. We introduce SNN, a practical machine learning-based relation analysis system, which can be used in different areas that require the analysis of relationships among sequenced data such as market basket analysis and online recommendation systems. In this paper, we present SNN-Cache that leverages SNN to utilize the inter-relationships among sequenced requests in caching decision. We evaluate SNN-Cache using an Information Centric Network (ICN) simulator, and show that it decreases the load of content servers significantly compared to a recent size-aware cache replacement algorithm (up to 30.7%) as well as the traditional cache replacement algorithms.