What you model is what you get: A model-driven dashboard generation approach

Context: Dashboards play a pivotal role in cloud systems monitoring, as they facilitate the visualization of the Key Performance Indicators (KPIs) that are continuously gathered from the system under observation. To timely and easily identify malfunctions and unexpected behaviors, operators have to configure, design, and maintain dashboards, so that the right set of indicators is properly visualized. Unfortunately, cost-effectively manipulating dashboards is a challenge, also for experts. Objectives: This paper proposes a model-driven approach that supports both the cost-effective definition (generation) and modification (adaptation) of dashboards. Method: The key idea is that a model-driven representation of a dashboard can be more easily manipulated than interacting with the GUI of dashboard management systems. Once a dashboard’s model is defined, the actual dashboard can be generated automatically with model-transformation techniques. Results: Our empirical results with popular Grafana Labs and Dynatrace dashboards show that the interpretability of the dashboards generated automatically is similar to the one of the manually configured dashboards. Moreover, the model-driven customization of the dashboard allows non-expert operators to act more efficiently, sometime as efficient as expert users. Conclusions: Overall results show that the model-driven approach can be used to cost-effectively generate useful dashboards, with an effectiveness close to that of experts.