End-to-end quality of service in edge computing environments

A system for end-to-end quality dataflow management in a network of an edge computing system, comprising:

• at least one processor;
• and
• memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to:
• collect delivery performance telemetry data for an ongoing quality dataflow between a first node and a second node of the edge computing system;
• calculate a current performance metric for the ongoing quality dataflow using the delivery performance telemetry data;
• compare the current performance metric to a target performance metric to determine an urgency value for the ongoing quality dataflow;
• evaluate the ongoing quality dataflow for dataflow delivery resources of the network with a resource adjustment model to determine a cost of available resource adjustments;
• and
• modify delivery of the ongoing quality dataflow using a resource adjustment, the resource adjustment selected based on an expected minimization of cost of delivery of the ongoing quality dataflow and the urgency value for the ongoing quality dataflow.

At least one non-transitory machine-readable medium including instructions for end-to-end quality dataflow management in a network of an edge computing system that, when executed by at least one processor, cause the at least one processor to perform operations to:

• collect delivery performance telemetry data for an ongoing quality dataflow between a first node and a second node of the edge computing system;
• calculate a current performance metric for the ongoing quality dataflow using the delivery performance telemetry data;
• compare the current performance metric to a target performance metric to determine an urgency value for the ongoing quality dataflow;
• evaluate the ongoing quality dataflow for dataflow delivery resources of the network with a resource adjustment model to determine a cost of available resource adjustments;
• and
• modify delivery of the ongoing quality dataflow using a resource adjustment, the resource adjustment selected based on an expected minimization of cost of delivery of the ongoing quality dataflow and the urgency value for the ongoing quality dataflow.