Synthetic Time Series for Anomaly Detection in Cloud Microservices

Synthetic Time Series for Anomaly Detection in Cloud Microservices

연구 분야: Software Development

학회: International Conference on Machine Learning, Optimization, and Data Science, Advanced Course and Symposium on Artificial Intelligence and Neuroscience

This paper proposes a framework for time series generation built to investigate anomaly detection in cloud microservices. In the field of cloud computing, ensuring the reliability of microservices is of paramount concern and yet a remarkably challenging task. Despite the large amount of research in this area, validation of anomaly detection algorithms in realistic environments is difficult to achieve. To address this challenge, we propose a framework to mimic the complex time series patterns representative of both normal and anomalous cloud microservices behaviors. We detail the pipeline implementation that allows deployment and management of microservices as well as the theoretical approach required to generate anomalies. Two datasets generated using the proposed framework have been made publicly available through GitHub.