Synthetic Time Series for Anomaly Detection in Cloud Microservices
This addresses the problem of realistic validation for researchers and practitioners in cloud computing, though it is incremental as it builds on existing data generation methods.
The paper tackles the challenge of validating anomaly detection algorithms for cloud microservices by proposing a framework to generate synthetic time series that mimic both normal and anomalous behaviors, resulting in two publicly available datasets.
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.