Low-latency systems to trigger remedial actions in data centers based on telemetry data

A hardware accelerator comprising:

• a processor;
• and
• a memory comprising instructions stored therein that, when executed by the processor, cause the processor to perform a set of actions comprising:
• receiving, via a first network, telemetry data generated by one or more sensors in a data center;
• generating training data for a machine-learning model stored at the hardware accelerator based on the telemetry data;
• training the machine-learning model based on the training data;
• receiving, via the first network, additional telemetry data generated via the one or more sensors;
• converting the additional telemetry data into a current input instance for the machine learning model;
• inputting the current input instance into the machine learning model;
• generating an output score via the machine learning model in response to the inputting and based on the current input instance;
• selecting a remedial action to apply within the data center in response to detecting that the output score satisfies a predefined condition;
• and
• sending, via the first network, a message that signals at least one computing device in the data center to execute the remedial action.

A method comprising:

• generating, via one or more sensors in a data center, telemetry data at one or more computing devices located in the data center;
• transmitting the telemetry data from the one or more computing devices located in the data center to a hardware accelerator located in the data center;
• generating training data for a machine-learning model stored at the hardware accelerator based on the telemetry data;
• training the machine-learning model based on the training data;
• receiving additional telemetry data from the one or more computing devices;
• converting the additional telemetry data into a current input instance for the machine learning model;
• inputting the current input instance into the machine learning model;
• generating an output score via the machine learning model in response to the inputting and based on the current input instance;
• selecting a remedial action to apply within the data center in response to detecting that the output score satisfies a predefined condition;
• and
• executing the remedial action within the data center.