Bi-level machine-learning-assisted management of computing resources

A computer-implemented method for machine learning (ML)-assisted allocation of a computing resource in a multi-tenant cloud environment comprising a plurality of instances of a network service, an application-level controller that utilizes a ML model to learn a performance target for a network service, and a service-level controller that adjusts an allocation of the computing resource to the network service based on the performance target, the method comprising:

• receiving, at the application-level controller, attributes of a network workload, resource allocation data describing the allocation of the computing resource to an instance of the network service, and a service-level objective (SLO);
• learning the performance target for the instance of the network service, by the ML model, based, at least in part, on the attributes of the network workload, the resource allocation data, and the SLO;
• receiving the performance target at the service-level controller;
• obtaining, by the service-level controller, resource utilization data associated with the instance of the network service;
• and
• adjusting the allocation of the computing resource to the instance of the network service, by the service-level controller, based, at least in part, on the performance target and the resource utilization data associated with the instance of the network service.

A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the one or more computing devices to:

• execute an application-level controller that utilizes a reinforcement machine learning (ML) model to learn a performance target for an instance of a network service based, at least in part, on
• attributes of a time-varying network workload that includes requests for the instance of the network service,
• resource allocation data describing an allocation of a computing resource to the instance of the network service, and
• a service-level objective (SLO) that defines a maximum latency for providing responses to the requests in the time-varying network workload;
• and
• execute a service-level controller that adjusts the allocation of the computing resource to the instance of the network service based, at least in part, on
• the performance target and
• resource utilization data associated with the network service.

A processing system, comprising:

• at least one processor;
• and
• at least one computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the at least one processor, cause the processing system to:
• execute a first controller that utilizes a machine learning (ML) model to learn a performance target for a network service based, at least in part, on
• attributes of a network workload that includes requests for the network service,
• resource allocation data describing an allocation of a computing resource to the network service, and
• a service-level objective (SLO);
• and
• execute a service-level controller that adjusts the allocation of the computing resource to the network service based, at least in part, on
• the performance target and resource utilization data associated with the network service.