Multivariate anomaly detection and root cause analysis of energy issues in microservice-based systems

Context: Microservice-based systems have become the architecture style of choice for modern applications, offering scalability, flexibility, and resilience. However, their distributed nature leads to increased resource consumption and energy inefficiencies, posing challenges for maintaining sustainable operations. Accurate anomaly detection (AD) and root cause analysis (RCA) tools are critical for diagnosing energy consumption issues in these systems, yet existing solutions often lack focus on energy metrics. Goal: This study aims to evaluate the effectiveness of AD and RCA algorithms in identifying and diagnosing performance-related energy consumption anomalies in microservice-based systems. Method: Two representative systems, Sock Shop and Train Ticket, are deployed under controlled environments. Then, anomalies are deliberately introduced by stressing at the same time CPU, memory, and disk resources. The data collection is conducted using Prometheus for performance metrics and Scaphandre for energy metrics. Once normal and anomalous datasets are constructed for each system, the study evaluates five AD algorithms (Birch, iForest, KNN, LOF, and SVM) and four RCA algorithms (MicroRCA, CausalRCA, CIRCA, and RCD) based on their precision, recall, and scalability across varied scenarios and workloads. Results: The experiment reveals that overall, iForest is the most effective AD algorithms in detecting energy anomalies (0.59 F-Score in Sock Shop and 0.634 F-Score in Train Ticket). In particular, iForest performs better in precision when the user load is high (1000 concurrent users). For RCA, CIRCA performs well in identifying root causes in smaller systems, while RCD is more scalable for larger and more complex systems. Conclusions: The findings of this study provide insights for both researchers and practitioners. In the context of our experiment, AD algorithms tend to perform relatively well, whereas RCA algorithms tend to be imprecise in localizing energy issues.