Distributed Consensus and Fault Tolerance Mechanisms Using Distributed Machine Learning

In this paper, we develop a distributed consensus model to improve the process of fault tolerance in cloud. The distributed consensus mechanism uses distributed machine learning as its base estimator to predict the fault instances when a task is allocated for possible offloading of user contents in the cloud. The distributed machine learning model senses the number of tasks and available nodes to complete the possible offloading of task in the cloud. The python simulator is conducted to test the efficacy of the distributed consensus mechanism in allocating the offloaded task without faults in the cloud servers. The results of simulation shows an increased prediction accuracy, reduced latency in offloading the task and classifying the fault tolerant VMs or task in process the task update.