Technologies for thermal and power awareness and management in a multi-edge cloud networking environment
Technologies for managing Function-as-a-Service function requests based on thermal and power awareness include an edge entity device having a circuitry to receive, from an edge device, a request to execute a function in an edge network environment having a plurality of edge entities. The circuitry is also to evaluate thermal and power criteria associated with the request and determine, as a function of a predicted thermal output over a specified time period relative to thermal and power criteria, whether to execute the function. In response to a determination by the circuitry to not execute the function, the circuitry is to select an edge entity of a plurality of edge entities that is able to satisfy the thermal and power criteria. The circuitry is further to forward the request to the selected edge entity.
1. An edge entity device in an edge network environment including a plurality of edge entities, the edge entity device comprising:
circuitry to:
obtain ambient sensor data from edge resource components, the ambient sensor data indicative of at least one of an energy harvested from the edge resource components, thermal metrics associated with the edge resource components, or battery power associated with the edge resource components;
generate a predictive model based on thermal and energy data obtained from ambient sensors associated with the edge resource components of the edge network environment and on historical sensor data associated with edge resource components, the generating of the predictive model including training the predictive model as a function of the obtained sensor data and the historical sensor data associated with the edge resource components;
receive, from an edge device in the edge network environment, a request to execute a function;
evaluate thermal and power criteria associated with the request;
determine, as a function of a predicted thermal output over a specified time period relative to the thermal and power criteria, whether to execute the function;
in response to a determination to not execute the function, select an edge entity of the plurality of edge entities that is able to satisfy the thermal and power criteria; and
forward the request to the selected edge entity.
2. The edge entity device of claim 1 , wherein to receive the request to execute the function, the circuitry is to receive a request to execute a function of a service, wherein the service is associated with a priority and a cost of execution according to a service level agreement.
3. The edge entity device of claim 2 , wherein to determine whether to execute the function, the circuitry is to determine, based on the priority, whether to execute the function.
4. The edge entity device of claim 1 , wherein to train the predictive model, the circuitry is to train the predictive model based on data originating from peer base stations and infrastructure sensors, wherein the circuitry is further to distribute the predictive model to each of the plurality of edge entities.
5. The edge entity device of claim 1 , wherein the circuitry is further to estimate, based on data subsequently obtained from the ambient sensors, the predicted thermal output over the specified time period.
6. The edge entity device of claim 5 , wherein to estimate the predicted thermal output, the circuitry is to estimate values indicative of expected thermal output and energy levels over the specified time period.
7. The edge entity device of claim 6 , wherein the circuitry is further to distribute the estimated predicted thermal output to each of the plurality of edge entities.
8. One or more non-transitory machine-readable storage media comprising a plurality of instructions, which, when executed, cause an edge entity device in an edge network environment including a plurality of edge entities to:
obtain ambient sensor data from edge resource components, the ambient sensor data indicative of at least one of an energy harvested from the edge resource components, thermal metrics associated with the edge resource components, or battery power associated with the edge resource components;
generate a predictive model based on thermal and energy data obtained from ambient sensors associated with the edge resource components of the edge network environment and on historical sensor data associated with edge resource components, the generating of the predictive model including training the predictive model as a function of the obtained sensor data and the historical sensor data associated with the edge resource components;
receive, from an edge device in the edge network environment, a request to execute a function;
evaluate thermal and power criteria associated with the request;
determine, as a function of a predicted thermal output over a specified time period relative to the thermal and power criteria, whether to execute the function;
in response to a determination to not execute the function, select an edge entity of the plurality of edge entities that is able to satisfy the thermal and power criteria; and
forward the request to the selected edge entity.
9. The one or more non-transitory machine-readable storage media of claim 8 , wherein the plurality of instructions, when executed, cause the edge entity device to receive the request to execute the function by executing a function of a service, wherein the service is associated with a priority and a cost of execution according to a service level agreement.
10. The one or more non-transitory machine-readable storage media of claim 9 , wherein the plurality of instructions, when executed, cause the edge entity device to determine whether to execute the function based on the priority.
11. The one or more non-transitory machine-readable storage media of claim 8 , wherein the plurality of instructions, when executed, cause the edge entity device to estimate, based on data subsequently obtained from the ambient sensors, the predicted thermal output over the specified time period.
12. The one or more non-transitory machine-readable storage media of claim 8 , wherein the plurality of instructions, when executed, cause the edge entity device to estimate the predicted thermal output by estimating values indicative of expected thermal output and energy levels over the specified time period.
13. The one or more non-transitory machine-readable storage media of claim 12 , wherein the plurality of instructions, when executed, cause the edge entity device to distribute the estimated predicted thermal output to each of the plurality of edge entities.