Constructing a Non-Linear Model with Neural Networks for Workload Characterization

Abstract

Workload characterization involves the understanding of the relationship between workload configurations and performance characteristics. To better assess the complexity of workload behavior, a model based approach is needed. Nevertheless, several configuration parameters and performance characteristics exhibit non-linear relationships that prohibit the development of an accurate application behavior model. In this paper, we propose a non-linear model based on an artificial neural network to explore such complex relationship. We achieved high accuracy and good predictability between configurations and performance characteristics when applying such a model to a 3-tier setup with response time restrictions. As shown by our work, a non-linear model and neural networks can increase the understandings of complex multi-tiered workloads, which further provide useful insights for performance engineers to tune their workloads for improving performance