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%.

Filed under: Deep Learning | Frequent Pattern Mining