Preventing service outages caused by service capacity constraints

A method comprising:

• accessing a training dataset that includes:
• first values associated with a traffic volume metric for a service executing in a cloud computing environment;
• and
• second values associated with a capacity metric for a resource supporting the service;
• generating, based on the training dataset, a service-specific machine learning model configured to project changes in capacity utilization of the resource supporting the service based on changes in traffic volume;
• accessing current values associated with the traffic volume metric for the service, wherein the current values reflect a current change in traffic volume;
• projecting, based on application of the service-specific machine learning model to the current values associated with the traffic volume metric for the service, a current change in capacity utilization of the resource supporting the service;
• determining that a capacity management policy is to be violated based on the current change in capacity utilization of the resource supporting the service;
• and
• providing a notification to a tenant that operates the service, the notification indicating that the capacity management policy is to be violated based on the current change in capacity utilization of the resource supporting the service.

A system comprising:

• a processing system;
• and
• a computer readable storage medium storing instructions that, when executed by the processing system, cause the system to perform operations comprising:
• accessing a training dataset that includes:
• first values associated with a traffic volume metric for a service executing in a cloud computing environment;
• and
• second values associated with a capacity metric for a resource supporting the service;
• generating, based on the training dataset, a service-specific machine learning model configured to project changes in capacity utilization of the resource supporting the service based on changes in traffic volume;
• accessing current values associated with the traffic volume metric for the service, wherein the current values reflect a current change in traffic volume;
• projecting, based on application of the service-specific machine learning model to the current values associated with the traffic volume metric for the service, a current change in capacity utilization of the resource supporting the service;
• determining that a capacity management policy is to be violated based on the current change in capacity utilization of the resource supporting the service;
• and
• implementing an action to cause an additional amount of the resource to be provisioned or an existing amount of the resource to be removed in response to determining that a capacity management policy is to be violated.