Abstract:

Failure prediction plays an important role for many tasks
such as optimal resource management in large-scale system.
However, accurately failure number prediction of repairable largescale
long-running computing (RLLC) is a challenge because of
the reparability and large-scale. To address the challenge, a general
Bayesian serial revision prediction method based on Bootstrap
approach and moving average approach is put forward, which can
make an accurately prediction for the failure number. To demonstrate
the performance gains of our method, extensive experiments
on the data of Los Alamos National Laboratory (LANL) cluster is
implemented, which is a typical RLLC system. And experimental
results show that the prediction accuracy of our method is 80.2 %,
and it is a greatly improvement with 4 % compared with some
typical methods. Finally, the managerial implications of the models
are discussed.