Data-Guided Approach for Learning and Improving User Experience in Computer Networks

Abstract

Machine learning algorithms have been traditionally used to understand user behavior or system performance. In computer networks, with a subset of input features as controllable network parameters, we envision developing a data-driven network resource allocation framework that can optimize user experience. In particular, we explore how to leverage a classifier learned from training instances to optimally guide network resource allocation to improve the overall performance on test instances. Based on logistic regression, we propose an optimal resource allocation algorithm, as well as heuristics with low-complexity. We evaluate the performance of the proposed algorithms using a synthetic Gaussian dataset, a real world dataset on video streaming over throttled networks, and a tier-one cellular operator’s customer complaint traces. The evaluation demonstrates the effectiveness of the proposed algorithms; e.g., the optimal algorithm can have a 400% improvement compared with the baseline.