de Silva, MDaniel, SKumarapeli, MMahadura, SRupasinghe, LLiyanapathirana, C2023-03-072023-03-072022-12-09M. d. Silva, S. Daniel, M. Kumarapeli, S. Mahadura, L. Rupasinghe and C. Liyanapathirana, "Anomaly Detection in Microservice Systems Using Autoencoders," 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2022, pp. 488-493, doi: 10.1109/ICAC57685.2022.10025259.979-8-3503-9809-0https://rda.sliit.lk/handle/123456789/3302The adaptation of microservice architecture has increased massively during the last few years with the emergence of the cloud. Containers have become a common choice for microservices architecture instead of VMs (Virtual Machines) due to their portability and optimized resource usage characteristics. Along with the containers, container-orchestration platforms are also becoming an integral part of microservice-based systems, considering the flexibility and scalability offered by the container-orchestration media. With the virtualized implementation and the dynamic attribute of modern microservice architecture, it has been a cumbersome task to implement a proper observability mechanism to detect abnormal behaviour using conventional monitoring tools, which are most suitable for static infrastructures. We present a system that will collect required data with the understanding of the dynamic attribute of the system and identify anomalies with efficient data analysis methods.enAnomaly DetectionMicroservice SystemsAutoencodersArticle10.1109/ICAC57685.2022.10025259