Policy-Induced Unsupervised Feature Selection: A Networking Case Study
Jalil Taghia, Farnaz Moradi, Hannes Larsson, Xiaoyu Lan, Masoumeh Ebrahimi, Andreas Johnsson · 2022
A promising approach for leveraging the flexibility and mitigating the complexity of future telecom systems is the use of machine learning (ML) models that can analyze the network performance, as well as taking proactive actions. A key enabler for ML models is timely access to reliable data, in terms of features, which require pervasive measurement points throughout the network. However, excessive monitoring is associated with network overhead. Considering domain knowledge may provide clues to find a balance between overhead reduction and meeting requirements on future ML use cases by monitoring just enough features. In this work, we propose a method of unsupervised feature selection that provides a structured approach in incorporation of the domain knowledge in terms of policies. Policies are provided to the method in form of must-have features defined as the features that need to be monitored at all times. We name such family of unsupervised feature selection the policy-induced unsupervised feature selection as the policies inform selection of the latent features. We evaluate the performance of the method on two rich sets of data traces collected from a data center and a 5G-mmWave testbed. Our empirical evaluations point at the effectiveness of the solution.