Predicting and reducing hardware related outages

A method comprising:

• obtaining, from a hardware component of a wireless telecommunications network, a performance indicator associated with the wireless telecommunications network, wherein the performance indicator comprises at least one of a system performance indicator or an application log;
• providing the performance indicator to a machine learning model trained to address a hardware error,
• wherein the machine learning model is trained to predict an occurrence of a hardware error and determine a resolution a resolution for the hardware error based on at least two of:
• prior hardware errors, a historical application log, historical system performance indicators, or a historical record of prior hardware errors comprising a plurality of issue tickets and a plurality of issue ticket resolutions, and wherein the machine learning model is trained by:
• obtaining the historical application log, the historical system performance indicators, or the historical record of prior hardware errors comprising the plurality of issue tickets and the plurality of issue ticket resolutions;
• and
• training the machine learning model to detect an anomaly, predict the occurrence of the hardware error, and obtain the resolution to the prior hardware error based on the historical application log, the historical system performance indicators, and the historical record of prior hardware errors comprising the plurality of issue tickets and the plurality of issue ticket resolutions;
• detecting, using the machine learning model, an anomaly associated with the performance indicator based on an infrequent occurrence in the performance indicator, wherein the anomaly is associated with a component of the wireless telecommunications network;
• identifying, using a service registry, one or more additional components likely to be affected by the anomaly, wherein the one or more additional components comprise components of the wireless telecommunications network that are connected to the component,
• wherein the service registry comprises a directed graph network, wherein each node of the directed graph network represents one or more components of the wireless telecommunications network, and wherein each edge of the directed graph network indicates dependencies between components of the wireless telecommunications network;
• and
• automatically notifying the one or more additional components of the anomaly, wherein the automatic notification prevents propagation of the hardware error to the one or more additional components;
• determining, using the machine learning model, a resolution to the anomaly based on a prior anomaly indicating a prior hardware error, wherein determining the resolution comprises determining similarity between the anomaly and the prior anomaly by fitting a curve to data points in a temporal window surrounding the anomaly, fitting a prior curve to data points in a prior temporal window surrounding the prior anomaly, and calculating a difference between the curve and the prior curve;
• and
• automatically executing the resolution to address the hardware error prior to the hardware error affecting users of the wireless telecommunications network.

A system comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, wherein the memory stores instructions executable by the one or more processors to:
• obtain, from a hardware component of a wireless telecommunications network, a performance indicator associated with the wireless telecommunications network, wherein the performance indicator includes at least one of a system performance indicator or an application log;
• provide the performance indicator to a machine learning model trained to address a hardware error,
• wherein the machine learning model is trained to predict an occurrence of a hardware error and determine a resolution for the hardware error based on at least two of:
• prior hardware errors, a historical application log, historical system performance indicators, or a historical record of prior hardware errors, the historical record of prior hardware errors comprising a plurality of issue tickets and a plurality of issue ticket resolutions, and wherein the machine learning model is trained by:
• obtaining the historical application log, the historical system performance indicators, or the historical record of prior hardware errors comprising the plurality of issue tickets and the plurality of issue ticket resolutions;
• and
• training the machine learning model to detect an anomaly, predict the occurrence of the hardware error, and obtain the resolution to the prior hardware error based on the historical application log, the historical system performance indicators, and the historical record of prior hardware errors comprising the plurality of issue tickets and the plurality of issue ticket resolutions;
• detect, using the machine learning model, an anomaly associated with the performance indicator based on an infrequent occurrence in the performance indicator;
• identify, using a service registry, one or more additional components likely to be affected by the anomaly, wherein the one or more additional components comprise components of the wireless telecommunications network that are connected to the component,
• wherein the service registry comprises a directed graph network, wherein each node of the directed graph network represents one or more components of the wireless telecommunications network, and wherein each edge of the directed graph network indicates dependencies between components of the wireless telecommunications network;
• and
• automatically notify the one or more additional components of the anomaly, wherein the automatic notification prevents propagation of the hardware error to the one or more additional components;
• determine, using the machine learning model, a resolution to the anomaly based on a prior anomaly indicating a prior hardware error, wherein determining the resolution comprises determining similarity between the anomaly and the prior anomaly by fitting a curve to data points in a temporal window surrounding the anomaly, fitting a prior curve to data points in a prior temporal window surrounding the prior anomaly, and calculating a difference between the curve and the prior curve;
• and
• automatically execute the resolution to address the hardware error prior to the hardware error affecting users of the wireless telecommunications network.