Content quality assessment and prediction via flows
Aspects of the subject disclosure may include, for example, analyzing content flow data in a communication network to obtain flow statistics; the statistics are aggregated to construct a model of the content flow based on the aggregated flow statistics. Operations of network nodes are predicted based on the model; a content delivery path in the communication network is identified. A quality of service is detected for network nodes on the content delivery path. The detected quality of service is compared with a predicted quality of service based on the model; a network anomaly is detected based on the comparing. The content flow on the content delivery path is adjusted responsive to detection of the network anomaly. Other embodiments are disclosed.
1. A method, comprising:
obtaining, by a processing system including a processor, a first model for operation of a network component to be deployed on a communication network;
analyzing, by the processing system, the operation of the network component on the communication network according to the first model, prior to deployment of the network component on the communication network;
predicting, by the processing system, performance of the network component in accordance with the analyzing;
obtaining, by the processing system, flow statistics regarding a content flow in the communication network, wherein the communication network includes the network component deployed as a network node;
constructing, by the processing system, a second model of the content flow based on the flow statistics;
detecting, by the processing system, a quality of service for each of a plurality of network nodes on a content delivery path, wherein the plurality of network nodes on the content delivery path comprise the network component;
comparing, by the processing system, the detected quality of service with a predicted quality of service based on the second model; and
adjusting, by the processing system, the content flow on the content delivery path responsive to detection of a network anomaly based on the comparing, wherein the adjusting the content flow comprises modifying the content delivery path by start-up of a first device to add a first network node, shutdown of a second device to remove a second network node, or a combination thereof.
2. The method of claim 1 , wherein the predicting is performed prior to the deployment of the network component.
3. The method of claim 1 , wherein the first model is based on performance models for a plurality of components other than the network component.
4. The method of claim 1 , wherein the communication network comprises the plurality of network nodes, the plurality of network nodes comprising devices, services, edge nodes, content sources, or a combination thereof.
5. The method of claim 1 , wherein the network anomaly is detected in accordance with a network metric exceeding a threshold.
6. The method of claim 5 , wherein the threshold is dynamically adjusted.
7. The method of claim 1 , further comprising predicting, by the processing system, a repair for the network component to mitigate the network anomaly.
8. The method of claim 7 , wherein the predicting the repair is performed prior to the deployment of the network component.
9. The method of claim 7 , further comprising providing, by the processing system, to equipment of a user a suggestion regarding the predicted repair.
10. The method of claim 7 , further comprising performing, by the processing system, a self-healing process including the repair.
11. A device comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising:
obtaining a first model for operation of a network component to be deployed on a communication network;
analyzing the operation of the network component on the communication network according to the first model, prior to deployment of the network component on the communication network;
predicting, prior to deployment of the network component on the communication network, performance of the network component in accordance with the analyzing;
obtaining flow statistics regarding a content flow in the communication network, wherein the communication network includes the network component deployed as a network node;
constructing a second model of the content flow based on the flow statistics;
detecting a quality of service for each of a plurality of network nodes on a content delivery path, wherein the plurality of network nodes on the content delivery path comprise the network component;
comparing the detected quality of service with a predicted quality of service based on the second model; and
adjusting the content flow on the content delivery path responsive to detection of a network anomaly based on the comparing, wherein the adjusting the content flow comprises modifying the content delivery path.
12. The device of claim 11 , wherein the modifying the content delivery path comprises a start-up of a first device to add a first network node, a shutdown of a second device to remove a second network node, or a combination thereof.
13. The device of claim 11 , wherein the communication network comprises the plurality of network nodes, the plurality of network nodes comprising devices, services, edge nodes, content sources, or a combination thereof.
14. The device of claim 11 , wherein the operations further comprise predicting a repair for the network component to mitigate the network anomaly.
15. The device of claim 14 , wherein the operations further comprise performing a self-healing process including the repair.
16. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system, facilitate performance of operations comprising:
obtaining a first model for operation of a network component to be deployed on a communication network;
analyzing the operation of the network component on the communication network according to the first model, prior to deployment of the network component on the communication network;
predicting performance of the network component in accordance with the analyzing;
obtaining flow statistics regarding a content flow in the communication network, wherein the communication network includes the network component deployed as a network node;
constructing a second model of the content flow based on the flow statistics;
detecting a quality of service for each of a plurality of network nodes on a content delivery path, wherein the plurality of network nodes on the content delivery path comprise the network component;
comparing the detected quality of service with a predicted quality of service based on the second model; and
adjusting the content flow on the content delivery path responsive to detection of a network anomaly based on the comparing, wherein the adjusting the content flow comprises modifying the content delivery path.
17. The non-transitory machine-readable medium of claim 16 , wherein the predicting is performed prior to the deployment of the network component.
18. The non-transitory machine-readable medium of claim 16 , wherein the modifying the content delivery path comprises a start-up of a first device to add a first network node, a shutdown of a second device to remove a second network node, or a combination thereof.
19. The non-transitory machine-readable medium of claim 16 , wherein the communication network comprises the plurality of network nodes, the plurality of network nodes comprising devices, services, edge nodes, content sources, or a combination thereof.
20. The non-transitory machine-readable medium of claim 16 , wherein the operations further comprise predicting a repair for the network component to mitigate the network anomaly.