Endpoint-Performance-Monitoring for a better End-User Experience

In an increasingly diverse and complex digital world a key challenge for companies is to maximize the productivity and motivation of their office workers. Thus, the task to measure, analyze and optimize the experience that these employees have with their digital devices becomes more and more important to ensure the competitiveness as well as the attractiveness of a company. In this paper end-user experience (EUE) includes measurable aspects such as boot-times, performance of tools and stability and availability of systems and software. In particular, for the IT administration, continuously optimizing the end-user experience is a considerable challenge. Our vision is to efficiently measure and quantify end-user experience and to automate the optimization of the infrastructure in order to support IT administrators. This paper shows an idea and a first concept for realization. A first step in measuring and evaluating end-user experience is to identify anomalies on endpoints. An endpoint can be any IT device used by the end-user. This paper presents a first implementation and evaluation of anomaly detection in IT infrastructures. First, the data collected on the endpoints is examined using a principal component analysis. Then, the data is analyzed for outliers using a neural network. For the implementation in this paper, an autoencoder is used. The evaluation of the results shows that an automated assessment of endpoint telemetry data using machine learning is possible. In summary, it is possible to detect anomalies in IT infrastructures using autoencoders. The anomalies in turn have an impact on the current or future end-user experience. In this way, autoencoder can be used in the future to improve the end-user experience of employees.