Anomaly Detection using Resource Behaviour Analysis for Autoscaling systems

In a cloud environment, autoscaling systems alleviate applications when additional resources are required. However, an illegitimate or malicious workload may force the system to automatically provision resources when they are not needed, thus leading to two key problems: economic denial of sustainability (eDoS) and wastage of resources. In this paper, we propose an anomaly detection mechanism using resource behaviour analysis to prevent these issues. We build univariate autoregressive statistical models to analyze resource behaviours for each microservice on the platform. The use of multiple models helps us discern unusual anomalies rather than a sudden increase in certain properties. We implemented the anomaly detection for the Elascale autoscaling engine on SAVI Testbed and evaluated the detection mechanisms against different attacks. From the results, we conclude that the models can accurately detect anomalous behaviour for applications (with cyclical trends) on the autoscaling platform.