Understanding the Energy Consumption of Cloud-native Software Systems
Lars Andringa, Brian Setz, Vasilios P. Andrikopoulos · 2025
As the dependence on software systems running on cloud data centers grows on a daily basis, there is an increasingly stronger motivation to reduce their energy consumption. A necessary but not trivial step in this direction is understanding how energy is consumed in virtualized, multi-tenant environments such as the one provisioned in the cloud. Prior work focuses on isolated, non-virtualized systems and is difficult to transfer to this context. A number of industry-led approaches have appeared in the meantime in terms of tools and technological stacks building on the concept of observability as the means to achieve this goal. This paper discusses our approach in adopting one such stack and consequently assessing it for fitness to purpose through an experimental procedure. To this effect, we deploy a cloud-native application on a private cloud infrastructure instrumented for measuring energy consumption through a combination of hardware and software means. We combine the information from these instrumentation points into a mapping model to deal with the different virtualization layers and compare the model against the values reported by the observability stack. Furthermore, we use our model to attribute energy consumption across the virtualization layers and understand how energy is consumed at each one.