From Prediction to Action: Improving User Experience With Data-Driven Resource Allocation

Abstract

Driven by the desire for a better user experience and enabled by improved data storage and processing, much of the recent work has studied user experience prediction in cellular networks. In this paper, moving beyond the prediction-only approach, we propose a data-driven resource allocation framework that uses data-generated prediction models to explicitly guide resource allocation for user experience improvement. In a closed-loop fashion, it further leverages and verifies the causal relation that often exists between certain feature values (e.g., bandwidth) and user experience in computer networks. As a case study, we consider how to reduce the number of user complaints in cellular networks. Our approach consists of three components: we train a logistic regression classifier to predict user experience, utilize the trained likelihood as the objective function to allocate network resource, and then evaluate user experience with allocated resource to (in)validate and adjust the original model. We design a DualHet algorithm to tackle the problem of multi-dimensional resource optimization with heterogeneous users. Numerical simulations based on both synthetic and real network data sets demonstrate the effectiveness of the proposed algorithms. In particular, the simulations based on real data demonstrate up to $2\times $ performance improvement compared with the baseline algorithm.