  With the increase in scale and complexity of ICT systems, their operation
increasingly requires automatic recovery from failures. Although it has become
possible to automatically detect anomalies and analyze root causes of failures
with current methods, making decisions on what commands should be executed to
recover from failures still depends on manual operation, which is quite
time-consuming. Toward automatic recovery, we propose a method of estimating
recovery commands by using Seq2Seq, a neural network model. This model learns
complex relationships between logs obtained from equipment and recovery
commands that operators executed in the past. When a new failure occurs, our
method estimates plausible commands that recover from the failure on the basis
of collected logs. We conducted experiments using a synthetic dataset and
realistic OpenStack dataset, demonstrating that our method can estimate
recovery commands with high accuracy.
