Metrics selection for load monitoring of service-oriented system

Background. Complex software systems produce a large amount of data depicting their internal state and activities. The data can be monitored to make estimations and predictions of the status of the system, helping taking preventative actions in case of impending malfunctions and failures. However, a complex system may reveal thousands of internal metrics, which makes it a non-trivial task to decide which metrics are the most important to monitor. Objective. In this work we aim at finding a subset of metrics to collect and analyse for the monitoring of the load in a Service-oriented system. Method. We use a performance test bench tool to generate load of different intensities on the target system, which is a specific service-oriented application platform. The numeric metrics data collected from the system is combined with the load intensity at each moment. The combined data is used to analyse which metrics are best at estimating the load of the system. By using a regression analysis it was possible to rank the metrics by their ability to measure the load of the system. Results. The results show that (1) the use of machine learning regressor allows to correctly measure the load of a service-oriented system, and (2) the most important metrics are related to network traffic and request counts, as well as memory usage and disk activity. Conclusion. The results help with the designs of efficient monitoring tool. In addition, further investigation should be focused on exploring more precise machine learning model to further improve the metric selection process.