PROCESSES AND SYSTEMS FOR FORECASTING METRIC DATA AND ANOMALY DETECTION IN A DISTRIBUTED COMPUTING SYSTEM

摘要

Computational processes and systems are directed to forecasting time series data and detection of anomalous behaving resources of a distributed computing system data. Processes and systems comprise off-line and on-line modes that accelerate the forecasting process and identification of anomalous behaving resources. In the off-line mode, recurrent neural network (“RNN”) is continuously trained using time series data associated with various resources of the distributed computing system. In the on-line mode, the latest RNN is used to forecast time series data for resources in a forecast time window and confidence bounds are computed over the forecast time window. The forecast time series data characterizes expected resource usage over the forecast time window so that usage of the resource may be adjusted. The confidence bounds may be used to detect anomalous behaving resources. Remedial measures may then be executed to correct problems indicated by the anomalous behavior.