  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.
