Automatically managing performance of software in a distributed computing environment

1. A system comprising:

one or more processors; and

one or more memories including instructions that are executable by the one or more processors for causing the one or more processors to:

deploy an instance of a service in a distributed computing environment using a definition file configured for deploying instances of the service;

receive metrics information describing resource usage by the service in the distributed computing environment;

determine a quality-of-service (QoS) constraint for the service by obtaining the QoS constraint from the definition file; and

subsequent to receiving the metrics information and determining the QoS constraint, automatically modify the definition file based on the metrics information and the QoS constraint.

2. The system of claim 1 , wherein the metrics information includes resource-usage metrics indicating memory usage, disk usage, processor usage, or network usage associated with the service.

3. The system of claim 1 , wherein the metrics information includes QoS metrics indicating a latency, a responsiveness, an availability, or a reliability of the service.

4. The system of claim 1 , wherein the QoS constraint is predefined in the definition file by a user.

5. The system of claim 1 , wherein the one or more memories further include instructions that are executable by the one or more processors for causing the one or more processors to:

provide the metrics information and the QoS constraint as input to a trained machine-learning model for receiving tuning information as output from the trained machine-learning model, the tuning information indicating an adjustment to a portion of the definition file for the service; and

modify the portion of the definition file based on the tuning information.

6. The system of claim 5 , wherein the portion of the definition file is a resource specification indicating how computing resources are to be allocated to the service.

7. The system of claim 1 , wherein the one or more memories further include instructions that are executable by the one or more processors for causing the one or more processors to iteratively perform a tuning process associated with the service, wherein each iteration of the tuning process involves:

receiving respective metrics information corresponding to the service; and

modifying one or more aspects of the definition file based on the respective metrics information and the QoS constraint.

8. The system of claim 1 , wherein the one or more memories further include instructions that are executable by the one or more processors for causing the one or more processors to:

deploy the service at a physical edge of the distributed computing environment, and wherein the service includes a serverless function.

9. A method comprising:

deploying, by one or more processors, an instance of a service in a distributed computing environment using a definition file configured for deploying instances of the service;

receiving, by the one or more processors, metrics information at a first time, the metrics information describing resource usage by the service in the distributed computing environment;

determining, by the one or more processors, a quality-of-service (QoS) constraint for the service; and

subsequent to receiving the metrics information and determining the QoS constraint:

determining, by the one or more processors, a modification to apply to the definition file, wherein the modification is determined based on the metrics information and the QoS constraint, and wherein the modification is configured to cause one or more instances of the service to more closely satisfy the QoS constraint at a subsequent time after the first time; and

automatically applying, by the one or more processors, the modification to the definition file.

10. The method of claim 9 , wherein the metrics information includes resource-usage metrics indicating memory usage, disk usage, processor usage, or network usage associated with the service.

11. The method of claim 9 , wherein the metrics information includes QoS metrics indicating a latency, a responsiveness, an availability, or a reliability of the service.

12. The method of claim 9 , further comprising determining the QoS constraint by obtaining the QoS constraint from the definition file.

13. The method of claim 12 , wherein the QoS constraint is predefined in the definition file by a user.

14. The method of claim 9 , further comprising:

providing the metrics information and the QoS constraint as input to a trained machine-learning model for receiving tuning information as output from the trained machine-learning model, the tuning information indicating an adjustment to a portion of the definition file for the service; and

modifying the portion of the definition file based on the tuning information.

15. The method of claim 14 , wherein the portion of the definition file is a resource specification indicating how computing resources are to be allocated to the service.

16. The method of claim 9 , further comprising iteratively performing a tuning process, wherein each iteration of the tuning process involves:

receiving respective metrics information corresponding to the service; and

modifying one or more aspects of the definition file based on the respective metrics information and the QoS constraint.

17. The method of claim 9 , further comprising deploying the service at a physical edge of the distributed computing environment, and wherein the service includes a serverless function.

18. A non-transitory computer-readable medium comprising program code that is executable by one or more processors for causing the one or more processors to:

deploy an instance of a service in a distributed computing environment using a definition file configured for deploying instances of the service;

receive metrics information at a first time, the metrics information describing resource usage by the service in the distributed computing environment;

determine a quality-of-service (QoS) constraint for the service; and

subsequent to receiving the metrics information and determining the QoS constraint:

determine a modification to apply to the definition file, wherein the modification is determined based on the metrics information and the QoS constraint, and wherein the modification is configured to cause one or more instances of the service to more closely satisfy the QoS constraint at a second time after the first time; and

automatically apply the modification to the definition file.

19. The non-transitory computer-readable medium of claim 18 , further comprising program code that is executable by the one or more processors for causing the one or more processors to:

determine the QoS constraint by obtaining the QoS constraint from the definition file.

20. The non-transitory computer-readable medium of claim 18 , further comprising program code that is executable by the one or more processors for causing the one or more processors to iteratively perform a tuning process associated with the service, wherein each iteration of the tuning process involves:

receiving respective metrics information corresponding to the service; and

applying a respective modification to one or more aspects of the definition file, the respective modification being determined based on the respective metrics information and the QoS constraint, wherein the respective modification is configured to cause the service to more closely satisfy the QoS constraint in a subsequent iteration of the tuning process than in a current iteration of the tuning process.