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Failure of a task running on a Hadoop cluster is highly expensive in terms of computational time. A failure occurring even at the end phase of the task may cause the need to redo the entire task. Thus is really important to deploy fault tolerant techniques. Hadoop deploys a technique of checkpointing to prevent data loss. However, computational time-loss still pose a grim threat to critical applications...
Hadoop architecture provides one level of fault tolerance, in a way of rescheduling the job on the faulty nodes to other nodes in the network. But, this approach is inefficient when a fault occurs after most of the job is executed. Thus, it's necessary to predict the fault at the node at quite an early stage so that the rescheduling of the job is not costly in terms of time and efficiency. Prediction...
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