Technologies for managing sensor and telemetry data on an edge networking platform

1. A device comprising:

circuitry to:

monitor telemetry data associated with a plurality of services provided in an edge network;

identify, for each of the plurality of services and as a function of the associated telemetry data, one or more service telemetry patterns;

generate a machine learning model to identify a first service telemetry pattern of change associated with a sensor data;

predict a second service telemetry pattern based on the generated machine learning model;

generate a profile including the first and second service telemetry patterns;

evaluate, for one of the plurality of services, the monitored telemetry data for a change in activity relative to the first and second service telemetry patterns; and

upon a determination that the change in activity is detected, perform an action responsive to the change.

2. The device of claim 1 , wherein to perform the action responsive to the change includes to determine the action as a function of an identified configuration state associated with the change in activity and a policy.

3. The device of claim 1 , wherein to perform the action responsive to the change further includes to reconfigure, as a function of the change, resources assigned to the one of the plurality of services.

4. The device of claim 1 , wherein to perform the action responsive to the change further includes to migrate the one of the plurality of services to a second device.

5. The device of claim 1 , wherein to monitor the telemetry data associated with the plurality of services provided in the edge network includes to monitor the telemetry data for one or more resources registered to a tenant.

6. The device of claim 5 , wherein the circuitry is further to store a public key associated with the tenant in a data store.

7. The device of claim 6 , wherein the circuitry is further to:

sign, using a private key associated with the one or more resources, the telemetry data and a corresponding timestamp; and

encrypt, using the public key associated with the tenant, the signed telemetry data and the corresponding timestamp.

8. The device of claim 5 , wherein the circuitry is further to:

monitor thermal telemetry data in the one or more resources;

evaluate the monitored thermal telemetry data in each of the one or more resources against a corresponding threshold; and

upon a determination that the corresponding threshold is exceeded, determine, from a service level agreement associated with the resources, a thermal budget for the one or more resources.

9. The device of claim 8 , wherein the circuitry is further to, upon a determination that the thermal budget is exceeded, notify an orchestrator device of the one or more resources exceeding the corresponding threshold.

10. The device of claim 8 , wherein the circuitry is further to, upon a determination that the thermal budget is not exceeded, perform one or more cooling techniques on the one or more resources to reduce a thermal load thereon.

11. The device of claim 1 , wherein the circuitry is further to:

receive sensor data from one or more edge devices connected with the edge network;

store the sensor data in a data store;

filter the stored sensor data; and

send the filtered sensor data to a core data center in the edge network.

12. The device of claim 11 , wherein to filter the stored sensor data, the circuitry is to:

identify, from the sensor data, one or more patterns; and

apply a filtering technique on the sensor data as a function of the identified one or more patterns.

13. The device of claim 12 , wherein to identify, from the sensor data, the one or more patterns, the circuitry is to identify, based on a machine learning technique, the one or more patterns.

14. The device of claim 12 , wherein to identify, from the sensor data, the one or more patterns, the circuitry is to identify, as a function of a type of the sensor data, the one or more patterns.

15. One or more machine-readable storage media storing a plurality of instructions, which, when executed, cause a device to:

monitor telemetry data associated with a plurality of services provided in an edge network;

identify, for each of the plurality of services and as a function of the associated telemetry data, one or more service telemetry patterns;

generate a machine learning model to identify a first service telemetry pattern of change associated with a sensor data;

predict a second service telemetry pattern based on the generated machine learning model;

generate a profile including the first and second service telemetry patterns;

evaluate, for one of the plurality of services, the monitored telemetry data for a change in activity relative to the first and second service telemetry patterns; and

upon a determination that the change in activity is detected, perform an action responsive to the change.

16. The one or more machine-readable storage media of claim 15 , wherein to monitor the telemetry data associated with a plurality of services provided in the edge network includes to monitor the telemetry data for one or more resources registered to a tenant, and wherein the plurality of instructions, when executed, cause the device to:

store a public key associated with the tenant in a data store;

sign, using a private key associated with the one or more resources, the telemetry data and a corresponding timestamp; and

encrypt, using the public key associated with the tenant, the signed telemetry data and the corresponding timestamp.

17. The one or more machine-readable storage media of claim 15 , wherein the plurality of instructions, when executed, cause the device to:

monitor thermal telemetry data in the one or more resources;

evaluate the monitored thermal telemetry data in each of the one or more resources against a corresponding threshold;

upon a determination that the corresponding threshold is exceeded, determine, from a service level agreement associated with the resources, a thermal budget for the one or more resources;

upon a determination that the thermal budget is exceeded, notify an orchestrator device of the one or more resources exceeding the corresponding threshold; and

upon a determination that the thermal budget is not exceeded, perform one or more cooling techniques on the one or more resources to reduce a thermal load thereon.

18. The one or more machine-readable storage media of claim 15 , wherein the plurality of instructions, when executed, cause the device to:

receive sensor data from one or more edge devices connected with the edge network;

store the sensor data in a data store;

identify, from the sensor data, one or more patterns;

apply a filtering technique on the sensor data as a function of the identified one or more patterns; and

send the sensor data with the filtering technique applied to a core data center in the edge network.

19. A device comprising:

means for monitoring telemetry data associated with a plurality of services provided in an edge network;

means for identifying to:

identify, for each of the plurality of services and as a function of the associated telemetry data, one or more service telemetry patterns;

generate a machine learning model to identify a first service telemetry pattern of change associated with a sensor data; and

predict a second service telemetry pattern based on the generated machine learning model; and

means for generating a profile including the identified first and second service telemetry patterns;

means for evaluating, for one of the plurality of services, the monitored telemetry data for a change in activity relative to the first and second service telemetry patterns; and

means for performing, upon a determination that the change in activity is detected, an action responsive to the change.