Deep Learning Models for Automated Identification of Scheduling Policies

Queueing network models are commonly used as performance models of distributed software applications and service-based systems. Although several methods exist for learning their parameters, such as demand estimation methods, little research has been carried out in the literature on automatically identifying scheduling policies from empirical datasets. Scheduling policies and their parameters have an impact on the model’s stationary distribution in general, thus their correct determination is important for model accuracy. They are particularly relevant for correctly estimating percentiles and higher-order moments of performance indexes such as response times. We propose a deep learning technique based on transformer models - a common technique in natural language processing, to address the lack of methods for this parameter identification problem. From a sample path of the joint network state, or an aggregate thereof, our approach can classify the scheduling policy of the stations in a queueing network. We show that the transformer model delivers good-classification precision and recall, improving significantly over support vector machines or simpler recurrent neural networks.