Anomaly Detection in Microservice-Based Systems

Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support effort. This study investigates the use of a supervised machine learning model, Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The covers creation microservices infrastructure, development fault injection module that simulates application-level service-level anomalies, system monitoring dataset, validation MLP model to detect anomalies. results indicate effectively detects anomalies both domains with higher accuracy, precision, recovery, F1 score on dataset. potential more effective management automation is highlighted this by focusing metrics such as service response times. provides valuable information about effectiveness models detecting across systems.