Microservice interaction prediction and anomaly detection in communcation platform as a service

In microservice-based telecommunication platforms with high number of users and heavy traffic, it is necessary to monitor the system, take quick action against possible and observed errors, and ensure the maintainability of the system. However, debugging on these platforms can take a long time. This difficulty arises from the need of understanding the behavior of microservices and detecting their interactions. In the first phase of this study, which aims to increase the efficiency of DevOps engineers on the work/time unit, it has been observed that providing microservice flows and interactions saves operation teams a significant amount of time during debugging. Accordingly, the study focused on microservice interactions and anomaly detection. First, different machine learning-based models predicting microservice interactions have been developed and their performances compared. In these models, log patterns are extracted on microservice log data and the interaction map of the mentioned microservice is created by estimating the previous and next microservices that the current microservice interacts with at a certain moment. In the next step, anomalous data were injected into the microservice logs, models were developed to detect these data and their performances were compared. In the experiments, successful estimation results were obtained that can contribute positively to the debugging process.