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

Sven Beckmann, Jonas Till, B. Bauer
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Abstract

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.
端点性能监控,以获得更好的终端用户体验
在一个日益多样化和复杂的数字世界中,公司面临的一个关键挑战是最大限度地提高员工的生产力和积极性。因此,衡量、分析和优化这些员工使用数字设备的体验对于确保公司的竞争力和吸引力变得越来越重要。在本文中,终端用户体验(EUE)包括可测量的方面,如启动时间、工具性能以及系统和软件的稳定性和可用性。特别是,对于IT管理来说,不断优化最终用户体验是一个相当大的挑战。我们的愿景是有效地度量和量化最终用户体验,并自动优化基础设施,以便为IT管理员提供支持。本文给出了一个思路和实现的初步概念。测量和评估终端用户体验的第一步是识别端点上的异常情况。端点可以是终端用户使用的任何IT设备。本文首次介绍了在IT基础设施中异常检测的实现和评价。首先,使用主成分分析检查在端点上收集的数据。然后,使用神经网络对数据进行异常值分析。在本文的实现中,使用了自编码器。对结果的评估表明,使用机器学习对端点遥测数据进行自动评估是可能的。总之,使用自动编码器可以检测it基础设施中的异常情况。这些异常反过来会对当前或未来的最终用户体验产生影响。通过这种方式,自动编码器可以在未来用于改善员工的最终用户体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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