AutoManager: a Meta-Learning Model for Network Management from Intertwined Forecasts

Abstract

A variety of network management and orchestration (MANO) tasks take advantage of predictions to support anticipatory decisions. In many practical scenarios, such predictions entail two largely overlooked challenges: (i) the exact relationship between the predicted values (e.g., reserved resources) and the performance objective (e.g., quality of experience of end users) is often tangled and cannot be known a priori, and (ii) the objective is linked in many cases to multiple predictions that contribute to it in an intertwined way (e.g., resources to reserved are limited and must be shared among competing flows). We present AutoManager, a novel meta-learning model that can support complex MANO tasks by addressing these two challenges. Our solution learns how multiple intertwined predictions affect a common performance goal, and steers them so as to attain the correct operation point under a-priori unknown loss functions. We demonstrate AutoManager in practical, complex use cases based on real-world traffic measurements; our experiments show that the model produces forecasts that are accurate and tailored to the MANO task in a fully automated way.