Identifying upgrades to an edge network by artificial intelligence
A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.
1. A computer-implemented method comprising:
identifying, in a network, a plurality of devices, wherein each device in the network is configured to provide data to at least one other device in the network;
determining capabilities of each device of the plurality of devices;
monitoring, for each device, capacity information and tasks performed during operation of the network;
analyzing, based on the monitoring, each use of each device, wherein the analyzing is configured to generate a Quality of Service (QoS) score for each device and an overall QoS score for the network, and wherein the QoS score for each device represents an effectiveness for each device;
recommending, in response to the analyzing and by a learning model, a first upgrade to the network, wherein the first upgrade is configured to increase an efficiency of the network; and
implementing the first upgrade, wherein the implementing includes adding a new device to the network.
2. The method of claim 1 , wherein the first upgrade is configured to increase a first QoS score for a first device of the plurality of devices.
3. The method of claim 1 , wherein the analyzing is performed by the learning model trained by a set of historical data based on the monitoring of each device.
4. The method of claim 1 , wherein the first upgrade includes upgrading a capacity of the network, and the recommending is based on the learning model predicting an increase in demand on the network.
5. The method of claim 3 , wherein the analyzing includes performing one or more simulations of the networks, and the training data is further based on results of the one or more simulations.
6. The method of claim 5 , wherein the one or more simulations include a digital twin for each device of the plurality of devices and an additional simulated device, wherein at least one setting on a first device is altered for the simulation.
7. The method of claim 1 , wherein the first upgrade includes adding a newly available product to the plurality of devices.
8. The method of claim 1 , wherein the first upgrade includes initiating an unused function on a second device of the plurality of devices.
9. The method of claim 1 , wherein the network is an Internet of Things (IoT) network, and the first upgrade includes changing the network to an edge network.
10. The method of claim 9 , wherein changing the network to the edge network includes adding an edge device to the plurality of devices.
11. A system comprising:
a processor; and
a computer-readable storage medium communicatively coupled to the processor and storing program instructions which, when executed by the processor, are configured to cause the processor to:
identify, in a network, a plurality of devices, wherein each device in the network is configured to provide data on at least one other device in the network;
determine capabilities of each device of the plurality of devices;
monitor, for each device, capacity information and tasks performed during operation of the network;
analyze, based on the monitoring, each use of each device, wherein the analysis is configured to generate a Quality of Service (QoS) score for each device and an overall QoS score for the network, and wherein the QoS score for each device represents an effectiveness for each device;
recommend, in response to the analysis and by a learning model, a first upgrade to the network, wherein the first upgrade is configured to increase an efficiency of the network; and
implement the first upgrade, wherein the implementing includes adding a new device to the network.
12. The system of claim 11 , wherein the analysis in performed by the learning model trained by a set of historical data based on the monitoring of each device.
13. The system of claim 12 , wherein the first upgrade can include upgrading a capacity of the network, and the recommendation is based on the learning model predicting an increase in demand on the network.
14. The system of claim 12 , wherein the analyzing includes performing one or more simulations of the networks, and the training data is further based on results of the one or simulations.
15. The system of claim 13 , wherein each of the one or more simulations includes generating a Quality of Service (QoS) score for each device in each simulation.
16. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing unit to cause the processing unit to:
identify, in a network, a plurality of devices, wherein each device in the network is configured to provide data on at least one other device in the network;
determine capabilities of each device of the plurality of devices;
monitor, for each device, capacity information and tasks performed during operation of the network;
analyze, based on the monitoring, each use of each device, wherein the analysis is configured to generate a Quality of Service (QoS) score for each device and an overall QoS score for the network, and wherein the QoS score for each device represents an effectiveness for each device;
recommend, in response to the analysis and by a learning model, a first upgrade to the network, wherein the first upgrade is configured to increase an efficiency of the network; and
implement the first upgrade, wherein the implementing includes adding a new device to the network.
17. The computer program product of claim 16 , wherein the plurality of devices includes a first device that is an Internet of Things device, a second device that is an edge computing device, and a third device that is a combined device, wherein the combined device includes an Internet of things device and an edge device in one device.
18. The computer program product of claim 17 , wherein the first upgrade includes replacing the first device with a new device, wherein the new device is a second combined device.
19. The computer program product of claim 17 , wherein the first upgrade includes replacing the first device and the second device with a second new device, wherein the second new device is a third combined device.