An anomaly detection framework for detecting anomalous virtual machines under cloud computing environment

Abstract

A variety of faults may cause performance degradation or even downtime of virtual machines (VMs) under Cloud environment, thus lowering the dependability of Cloud platform. Detecting anomalous VMs before real failures occur is an important means to improve the dependability of Cloud platform. Since the performance or state of VMs may be affected by the environmental factors, this article proposes an environment-aware anomaly detection framework (termed EaAD) for VMs under Cloud environment. EaAD partitions all the VMs in Cloud platform into several monitoring domains based on similarity in running environment, which makes the VMs in a same monitoring domain have similar running environment. In each domain, the equipped anomaly detection algorithm detects anomalous VMs based on their performance metrics. In addition, anomaly detection in a certain monitoring domain faces such challenges as multiple anomaly categories, imbalanced training sample sets, increasing number of training samples. To cope with these challenges, several support vector machine (SVM) based anomaly detection algorithms are implemented and equipped in EaAD, including C-SVM, OCSVM, multi-class SVM, imbalanced SVM, online learning SVM. This article conducts experiments on EaAD to test the performance of the adopted detection algorithms and looks into future work.