Edge AI is an architectural deployment tactic that brings AI models closer to the user and data, relieving internet bandwidth usage and providing low latency and privacy. It remains unclear how this tactic performs at scale, since the distribution overhead could impact the total energy consumption. We identify four architectural scalability factors that could impact the energy consumption of AI: environment, optimisation, throughput, and overhead. The latter consists of downloading, verification, and updating the model over time. This work performs an empirical study on the sustainability of Edge AI compared to Cloud AI at scale in terms of energy consumption. For the environment variable, energy consumption measurement experiments are run on a cloud device and multiple edge devices, various quantized models for optimisation, and various throughput levels per hour. We simulate the distribution overhead and combine the results with the measurements to find the holistic energy efficiency of each architectural strategy. We find that all four variables impact energy consumption, but the main contributors are environment, throughput, and overhead. We observe that Edge AI is most energy-efficient in low-distribution, low-demand scenarios, whereas in high-distribution, high-demand scenarios Cloud AI is better optimised and outperforms Edge AI in energy efficiency. This means that developers depending on their use case and the project’s scalability need to consider these quality attributes for the most sustainable architectural solution.

TOPdesk is a service management software provider in a wide variety of domains and industries. TOPdesk also offers consultancy to their customers that aims to continuously assess and improve the customer’s experience and service efficiency. TOPdesk offers a Mini Health Check (MHC) to their customers in which aconsultant analyzes how efficiently the customer uses their software based on six Key Performance Indicators (KPI). However, the process of creating an MHC report is very time-consuming as it requires performing a lot of manual steps. Also, the norms used for the KPIs provide little meaning as they are arbitrarily chosen and not specific to the customer’s industry. This report aims to improve the current process of performing an MHC. Research has been done on how the MHC is performed, identifying the suitable technologies and learning the currently existing infrastructure that helped us pave the way to create our product. During our project we managed to create a product that automates the MHC. Through user testing we found that this process now takes about two minutes, where the manual process took about two hours. To create more meaningful norms for the KPIs, we also implemented a benchmarking feature. This allows a company to compare the results of their MHC to other TOPdesk customers in the same sector, country or of similar size. We have some recommendations for TOPdesk for the further development of our product. The MHC process could be streamlined in a few ways, most importantly with respect to the process for getting access to customer data. Benchmarking could become even more useful if data can be more easily gathered from more TOPdesk customers.