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With the increase in scale and complexity of ICT systems, their operationincreasingly requires automatic recovery from failures. Although it has becomepossible to automatically detect anomalies and analyze root causes of failureswith current methods, making decisions on what commands should be executed torecover from failures still depends on manual operation, which is quitetime-consuming. Toward automatic recovery, we propose a method of estimatingrecovery commands by using Seq2Seq, a neural network model. This model learnscomplex relationships between logs obtained from equipment and recoverycommands that operators executed in the past. When a new failure occurs, ourmethod estimates plausible commands that recover from the failure on the basisof collected logs. We conducted experiments using a synthetic dataset andrealistic OpenStack dataset, demonstrating that our method can estimaterecovery commands with high accuracy.

Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning