Methods and systems for detecting and reporting anomalous behavior of objects running in a data center

A computer system for detecting and reporting anomalous behavior of an object running in a data center, the computer system comprising:

• one or more processors;
• one or more data-storage devices;
• and
• machine-readable instructions stored in the one or more data-storage devices that when executed using the one or more processors control the system to performance operations comprising:
• using a trained deep neural network (“DNN”) to generate forecast metric values of a metric of the object in a time interval from historical metric values of the metric;
• for each runtime metric value of the metric generated in the time interval, computing a runtime residual value based on the runtime metric value and a forecast metric value at a time stamp in the time interval;
• determining in real time whether the runtime residual value indicates anomalous behavior of the object using spectral residual anomaly detection;
• and
• in response to detecting anomalous behavior of the object, displaying in a graphical user interface (“GUI”) of an electronic display device an alert that identifies anomalous behavior of the object, the runtime metric value, and a time stamp of the anomalous behavior.

A non-transitory computer-readable medium having instructions encoded thereon for enabling one or more processors of a computer system to detect and report anomalous behavior of an object running in a data center by performing operations comprising:

• using a trained deep neural network (“DNN”) to generate forecast metric values of a metric of the object in a time interval from historical metric values of the metric;
• for each runtime metric value of the metric generated in the time interval, computing a runtime residual value based on the runtime metric value and a forecast metric value at a time stamp in the time interval;
• determining in real time whether the runtime residual value indicates anomalous behavior of the object using spectral residual anomaly detection;
• and
• in response to detecting anomalous behavior of the object, displaying in a graphical user interface (“GUI”) of an electronic display device an alert that identifies anomalous behavior of the object, the runtime metric value, and a time stamp of the anomalous behavior.