Detecting failures early in cloud-based software systems is highly significant as it can reduce operational costs, enhance service reliability, and improve user experience. Many existing approaches include anomaly detection in metrics or a blend of metric and log features. However, such approaches tend to be very complex and hardly explainable, and consequently non-trivial for implementation and evaluation in industrial contexts. In collaboration with a case company and their cloud-based system in the domain of PIM (Product Information Management), we propose and implement autonomous monitors for proactive monitoring across multiple services of distributed software architecture, fused with anomaly detection in performance metrics and log analysis using GPT-3. We demonstrated that operations engineers tend to be more efficient by having access to interpretable alert notifications based on detected anomalies that contain information about implications and potential root causes. Additionally, proposed autonomous monitors turned out to be beneficial for the timely identification and revision of potential issues before they propagate and cause severe consequences.

Monitoring a microservice system may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. In this study, we explore deep learning (DL) solutions for detection of anomalous system’s behavior based on collected monitoring data that consists of applications’ and systems’ performance metrics. The study is conducted in a collaboration with a Swedish company responsible for ticket and payment management in public transportation. Moreover, we specifically address a shortage of approaches for evaluating DL models without any ground truth data. Hence, we propose a solution design for anomaly detection and reporting alerts inspired by state-of-the-art DL solutions. Furthermore, we propose a plan for its in-context implementation and evaluation empowered by feedback from the development team. Through continuous feedback from development, the labeled data is generated and used for optimization of the DL model. In this way, a microservice system may leverage DL solutions to address rising challenges within its architecture. CCS CONCEPTS • Software and its engineering → Software post-development issues; • Information systems → Data mining; Computing platforms; • Computing methodologies → Machine learning.

The design of a disturbance observer (DOB) for a nonlinear half-car hydraulic active suspension system is presented. The system is controlled by the flatness-based controller (FBC) which is capable to provide efficient control under deterministic road disturbances and model uncertainties. The main objective is to design the mutually coupled general-proportional-integral (GPI) disturbance observers which estimate the disturbances in on-line mode during the car driving. It is assumed that the nonlinear effects, parameter variations, road profiles and possibly input unmodeled dynamics are lumped into an unknown time-varying and stochastic disturbance input signal. The quality and effectiveness of the proposed GPI-based DOB are shown by simulation results.