IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment
This addresses the challenge of ensuring cloud infrastructure reliability for users and providers by enabling indirect anomaly detection, though it is incremental as it builds on existing machine learning methods for a specific bottleneck.
The paper tackles the problem of detecting anomalous Virtual Machine Monitors (VMMs) in cloud environments without direct access to VMM data by proposing the IAD algorithm, which uses VM resource utilization data and achieves an average F1-score of 83.7%, outperforming other algorithms by 11%.
Server virtualization in the form of virtual machines (VMs) with the use of a hypervisor or a Virtual Machine Monitor (VMM) is an essential part of cloud computing technology to provide infrastructure-as-a-service (IaaS). A fault or an anomaly in the VMM can propagate to the VMs hosted on it and ultimately affect the availability and reliability of the applications running on those VMs. Therefore, identifying and eventually resolving it quickly is highly important. However, anomalous VMM detection is a challenge in the cloud environment since the user does not have access to the VMM. This paper addresses this challenge of anomalous VMM detection in the cloud-based environment without having any knowledge or data from VMM by introducing a novel machine learning-based algorithm called IAD: Indirect Anomalous VMMs Detection. This algorithm solely uses the VM's resources utilization data hosted on those VMMs for the anomalous VMMs detection. The developed algorithm's accuracy was tested on four datasets comprising the synthetic and real and compared against four other popular algorithms, which can also be used to the described problem. It was found that the proposed IAD algorithm has an average F1-score of 83.7% averaged across four datasets, and also outperforms other algorithms by an average F1-score of 11\%.