IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment

TL;DR

This paper introduces IAD, a machine learning algorithm that detects anomalous virtual machine monitors indirectly through VM resource data, addressing a key challenge in cloud environments without direct VMM access.

Contribution

The paper presents a novel indirect detection method for anomalous VMMs using only VM resource data, outperforming existing algorithms in accuracy.

Findings

IAD achieves an average F1-score of 83.7%.

It outperforms other algorithms by an average of 11% F1-score.

The method is effective on both synthetic and real datasets.

Abstract

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\%.