Synthetic Time Series for Anomaly Detection in Cloud Microservices

TL;DR

This paper introduces a framework for generating synthetic time series data to evaluate anomaly detection methods in cloud microservices, addressing validation challenges in realistic scenarios.

Contribution

The paper presents a novel framework for creating realistic synthetic time series data for anomaly detection in cloud microservices environments.

Findings

Two datasets generated using the framework are publicly available.

The framework effectively mimics normal and anomalous microservice behaviors.

Provides a pipeline for deploying microservices and generating anomalies.

Abstract

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.