  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.
