Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach
This work addresses resource management challenges for industrial practitioners by enabling interpretable prediction of user transfer, though it appears incremental as it builds on existing Bayesian nonparametric methods.
The paper tackles the problem of characterizing user transfer patterns amid resource variation in industrial settings, proposing CLUSTER, a Bayesian nonparametric model that identifies user clusters and predicts transfers with alignment to empirical data in communications industry experiments.
In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development.