Anomaly Detection in High-Performance API Gateways

Abstract

Detecting performance anomalies and taking corrective or preventive actions are key requirements in high-performance software systems. However, progress in research related to performance anomaly detection has been limited due to the lack of publicly available datasets. This holds true for performance anomaly detection in API gateways as well. With the advent of the API Economy, API gateways are likely to be widely deployed, thus becoming a key component of enterprise integration architectures. Therefore, it is important to detect performance anomalies in such high-performance API-Gateway systems. The primary contribution of the paper is Vichalana, a dataset that can be used to evaluate the accuracy of anomaly detection algorithms in API-Gateways. In order to generate this data set we first classify the anomalies in API-Gateways into 7 types. Second, we provide detailed criteria for re-creating them in API-Gateway environments. Third, we re-create these anomaly types in an API-Gateway environment (similar to a production environment) and collect 25 measurement parameters relating to CPU, memory, network IO and disk IO under both normal and anomalous conditions. The data set we provide is based on the data we collect in these tests. Finally, using several example scenarios, we illustrate the behaviour of several measurement parameters under different anomaly types. We provide the reasoning for particular behaviours of parameters for different types of anomalous behaviours.