Guyot: a hybrid learning- and model-based RTT predictive approach

Abstract

Knowing the Round-Trip Time (RTT) between a client and a server is important for an online interactive multi-media service to provide satisfactory quality of user experience. Due to the intrinsic dynamics of the topology and routing strategies in the Internet, it is however challenging to predict the RTT accurately from limited information, e.g., only the IP pair of the client and server. To address this challenge, we propose Guyot, a hybrid learning- and model-based approach to predict RTT, which requires significantly smaller amount of data to be collected than traditional approaches, while achieving a similar prediction accuracy. Our design is based on a large-scale measurement study from a content provider's perspective. Based on an information gain analysis, we design a hybrid RTT prediction approach involving two types of predictions: (1) Learning-based prediction: We train a decision tree to predict RTT between IP pairs with large geographic distance, requiring only a small set of features to be collected. (2) Model-based prediction: We use a model-based framework to predict RTT between IP pairs with small distance, providing an accurate RTT prediction over time. By strategically dividing RTT prediction tasks to these two types according to the distance of the inferred geo-locations of the IPs, our prediction approach can scale with satisfactory accuracy. Our experiments further confirm the superiority of our design.