AFDI: A Virtualization-based Accelerated Fault Diagnosis Innovation for High Availability Computing

TL;DR

This paper introduces AFDI, a virtualization-based hybrid fault diagnosis model that efficiently monitors system metrics to improve fault detection accuracy and system availability in cloud environments.

Contribution

The paper presents a novel hybrid fault diagnosis model combining MDD, NBC, and virtual sensors to reduce traffic overhead and enhance fault detection in cloud virtualization.

Findings

Improved fault detection accuracy in cloud environments.

Reduced traffic overhead compared to active probing methods.

Enhanced system availability through hybrid diagnosis approach.

Abstract

Fault diagnosis has attracted extensive attention for its importance in the exceedingly fault management framework for cloud virtualization, despite the fact that fault diagnosis becomes more difficult due to the increasing scalability and complexity in a heterogeneous environment for a virtualization technique. Most existing fault diagnoses methods are based on active probing techniques which can be used to detect the faults rapidly and precisely. However, most of those methods suffer from the limitation of traffic overhead and diagnosis of faults, which leads to a reduction in system performance. In this paper, we propose a new hybrid model named accelerated fault diagnosis invention (AFDI) to monitor various system metrics for VMs and physical server hosting, such as CPU, memory, and network usages based on the severity of fault levels and anomalies. The proposed method takes the advantages of the multi-valued decision diagram (MDD), A Naive Bayes Classifier (NBC) models and virtual sensors cloud to achieve high availability for cloud services.