Predicting network anomaly events

A non-transitory computer-readable storage medium with program instructions stored thereon, the program instructions when executed by one or more processors are operable to cause the one or more processors to perform operations comprising:

• analyzing data metrics associated with a computer communication network, wherein the data metrics relate to at least one or a CPU utilization metric, a disk utilization metric and a memory utilization metric;
• and
• predicting, based on a machine learning model, a time and an occurrence of a network anomaly event, wherein the machine learning model is based on a set of data metrics associated with the computer communication network and network anomaly event data associated with the computer communication network,
• wherein the network anomaly event data includes a detection of a system failure or abnormal incident in the computer communication network, and
• wherein the machine learning model is trained by generating at least one training prediction using the set of data metrics and adjusting the machine learning model based on a comparison between the at least one training prediction and a known network event result.

A system comprising:

• one or more processors;
• and
• logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors and when executed operable to cause the one or more processors to perform operations comprising:
• generating a machine learning model for a computer communication network by:
• applying a set of time series data metrics that is a sequence of hardware resource metrics collected over a constant time interval of the computer communication network to train the machine learning model, and
• applying network anomaly event data that includes a detection of a system failure or abnormal incident in the computer communication network, to train the machine learning model;
• determining a data metric threshold for the computer communication network, wherein the data metric threshold is to indicate a limit for future data metrics associated with the computer communication network;
• analyzing the data metrics associated with the computer communication network;
• predicting, by using the machine learning model, a first future time when the data metrics associated with the computer communication network will meet or exceed the data metric threshold value determined for the computer communication network,
• wherein the machine learning model is trained by generating at least one training prediction using the set of time series data metrics and adjusting the machine learning model based on a comparison between the at least one training prediction and a known network event result;
• and
• predicting a second future time when a network anomaly event will occur based on the network anomaly event data.