A Closed-Loop Approach in Data-Driven Resource Allocation to Improve Network User Experience
Yanan Bao*, University of California, Davi; Huasen Wu, UC Davis; Xin Liu, UC Davis
Machine learning methods have been widely used in modeling and predicting network user experience. In this pa-per, moving beyond user experience prediction, we propose a closed-loop approach that uses data-generated prediction models to explicitly guide resource allocation for user experience improvement. The closed-loop approach leverages and veriﬁes the causal relation that often exists between certain feature values (e.g., bandwidth) and user experience in computer networks. The approach consists of three components: we train a neural network classiﬁer to predict user experience, utilize the trained neural network classiﬁer as the objective function to allocate network resource, and then evaluate user experience with allocated resource to (in)validate and adjust the original model. Speciﬁcally, we propose a dual decomposition algorithm to solve the neural network-based resource optimization problem, which is complex and non-convex. We further develop an iterative mechanism for classiﬁer optimization. Numerical results show that the dual algorithm reduces the expected number of unsatisﬁed users by up to 2x compared with the baseline, and the optimized classiﬁer further improves the performance by 50%.