AFDI: A Virtualization-based Accelerated Fault Diagnosis Innovation for High Availability Computing
This work addresses fault management for cloud computing systems, but it appears incremental as it builds on existing techniques like MDD and Naive Bayes Classifier.
The paper tackles the challenge of fault diagnosis in cloud virtualization environments, where existing methods cause traffic overhead and reduce system performance, by proposing AFDI, a hybrid model that monitors system metrics and achieves high availability for cloud services.
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.