Artificial Neural Network Based Fault Prediction and Detection in Grid Computing

Reliability is a very well-known matter in now day's Grid systems and it is anticipated to become still more difficult in the next generation systems. Because the ongoing fault tolerance approaches like checkpoint and replication techniques are examined to be ineffectual due to performance and suitability issues, improved fault tolerance approaches are today under inspection. The fault tolerance used taking place fault prediction and detection in organize to minimize collision of failure on system and detect faulty and non-faulty resources. In this research, we traverse the tradition of artificial neural network for fault prediction and fault detection improvement in a fault tolerance context. Outcomes display the prediction and detection performance improvement of the prior thresholds trigger and classifying approach.