Abstract: Techniques are provided for automatically correcting computing resource overutilization. In one example, a computing device receives an indication of a utilization exceeding a preset threshold. The computing device determines, using a machine-learning model, an action responsive to the utilization. The computing device outputs a command to execute the action responsive to the utilization and then determines that the utilization no longer exceeds the preset threshold.

Abstract: A system can include instructions for generating a machine-learning model, including accessing, from a memory device, training data including two or more first commands. The first commands can be manually generated in response to a utilization exceeding a preset threshold and two or more first conditions, including a date and time the utilization exceeded the preset threshold. The training data can also include second commands generated by the machine-learning model in response to the utilization exceeding the preset threshold. The system may train the machine-learning model using a machine-learning algorithm using the training data and the second commands. The system may receive an indication of a utilization exceeding a preset threshold and determine, using the machine-learning model, an action responsive to the utilization. A command can be outputted to execute the action responsive to the utilization.