A New Theoretical Approach: A Model Construct for Fault Troubleshooting in Cloud Computing

Abstract

In cloud computing, there are four effective measurement criteria: (I) priority, (II) fault probability, (III) risk, and (IV) the duration of the repair action determining the efficacy of troubleshooting. In this paper, we propose a new theoretical algorithm to construct a model for fault troubleshooting; we do this by combining a Naïve-Bayes classifier (NBC) with a multivalued decision diagram (MDD) and influence diagram (ID), which structure and manage problems related to unambiguous modeling for any connection between significant entities. First, the NBC establish the fault probability based on a Naïve-Bayes probabilistic model for fault diagnosis. This approach consists of three steps: (I) identifying the network parameters to also show the reliance for probability relationship among the entire set of nodes; (II) determining the structure of the network topology; (III) assessing the probability of the fault being propagated. This calculates the probability of each node being faulty given the evidence. Second, the MDD combines the influential factors of four measurements and determines the utility value of prioritizing their actions during each step of the fault troubleshooting which in turn assesses which fault is selected for repair. We demonstrate how the procedure is adapted by our method, with the host server's failure to initiate a case-study. This approach is highly efficient and enables low-risk fault troubleshooting in the field of cloud computing.