System performance evaluation and enhancement in a software-defined system
Performance of devices can be evaluated and enhanced in software-defined systems. For example, a computing device can receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system. The computing device can compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system. The computing device can determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system. The computing device can output, by the server, an indication of the adjustment to the client system.
1. A system comprising:
a processor; and
a memory including instructions that are executable by the processor for causing the processor to:
receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system;
compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system;
determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system; and
output, by the server, an indication of the adjustment to the client system.
2. The system of claim 1 , wherein the memory further includes instructions that are executable by the processor for causing the processor to output the indication for causing the client system to automatically implement the adjustment.
3. The system of claim 1 , wherein the memory further includes instructions that are executable by the processor for causing the processor to determine the adjustment by providing the first plurality of properties and the additional properties to a machine-learning model.
4. The system of claim 1 , wherein the memory further includes instructions that are executable by the processor for causing the processor to:
assign an anonymous identifier to the client system and each of the at least one additional client system prior to receiving the first plurality of properties and the additional properties.
5. The system of claim 1 , wherein the first plurality of properties comprises configuration information, workload information, and at least one performance parameter for the client system.
6. The system of claim 1 , wherein the memory further includes instructions that are executable by the processor for causing the processor to determine the similarity of the client system to each of the at least one additional client system by applying a clustering algorithm to the first plurality of properties and the additional properties.
7. The system of claim 1 , wherein the memory further includes instructions that are executable by the processor for causing the processor to:
monitor the client system subsequent to the client system implementing the adjustment; and
determine a classification of the adjustment for a subsequent adjustment determination for another client system.
8. A method comprising:
receiving, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system;
comparing, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system;
determining, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system; and
outputting, by the server, an indication of the adjustment to the client system.
9. The method of claim 8 , further comprising:
outputting the indication for causing the client system to automatically implement the adjustment.
10. The method of claim 8 , further comprising:
determining the adjustment by providing the first plurality of properties and the additional properties to a machine-learning model.
11. The method of claim 8 , further comprising:
assigning an anonymous identifier to the client system and each of the at least one additional client system prior to receiving the first plurality of properties and the additional properties.
12. The method of claim 8 , wherein the first plurality of properties comprises configuration information, workload information, and at least one performance parameter for the client system.
13. The method of claim 8 , further comprising:
determining the similarity of the client system to each of the at least one additional client system by applying a clustering algorithm to the first plurality of properties and the additional properties.
14. The method of claim 8 , further comprising:
monitoring the client system subsequent to the client system implementing the adjustment; and
determining a classification of the adjustment for a subsequent adjustment determination for another client system.
15. A non-transitory computer-readable medium comprising program code that is executable by a processor for causing the processor to:
receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system;
compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system;
determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system; and
output, by the server, an indication of the adjustment to the client system.
16. The non-transitory computer-readable medium of claim 15 , further comprising program code that is executable by the processor for causing the processor to output the indication for causing the client system to automatically implement the adjustment.
17. The non-transitory computer-readable medium of claim 15 , further comprising program code that is executable by the processor for causing the processor to determine the adjustment by providing the first plurality of properties and the additional properties to a machine-learning model.
18. The non-transitory computer-readable medium of claim 15 , further comprising program code that is executable by the processor for causing the processor to:
assign an anonymous identifier to the client system and each of the at least one additional client system prior to receiving the first plurality of properties and the additional properties.
19. The non-transitory computer-readable medium of claim 15 , further comprising program code that is executable by the processor for causing the processor to determine the similarity of the client system to each of the at least one additional client system by applying a clustering algorithm to the first plurality of properties and the additional properties.
20. The non-transitory computer-readable medium of claim 15 , further comprising program code that is executable by the processor for causing the processor to:
monitor the client system subsequent to the client system implementing the adjustment; and
determine a classification of the adjustment for a subsequent adjustment determination for another client system.