Intelligent garbage collector for containers

1. A method for performing garbage collection comprising:

providing a garbage collection data structure, the garbage collection data structure including metadata and one or more resource consumption parameters corresponding to a container being executed on a host machine;

analyzing, by a machine-learning function, the metadata and the one or more resource consumption parameters;

classifying, based on the analyzing, the container into one or more classes, the one or more classes including at least one of a suspicious container class, a malicious container class, or a normal container class; and

performing, based on the classifying, one or more garbage collection actions on the container, including at least one of generating an alert corresponding to the container, reducing a resource consumption of the container, or marking the container as safe.

2. The method of claim 1 , wherein the machine-learning function includes a decision tree, and wherein a first attribute of the decision tree includes a first one of the one or more resource consumption parameters, and a second attribute of the decision tree includes a second one of the one or more resource consumption parameters.

3. The method of claim 1 , wherein the one or more garbage collection actions for reducing the resource consumption of the container includes stopping execution of the container.

4. The method of claim 1 , wherein the one or more garbage collection actions for reducing the resource consumption of the container includes deleting the container.

5. The method of claim 1 , wherein the one or more resource consumption parameters include a processor usage, a memory usage, or a duration corresponding to the container.

6. The method of claim 1 , wherein the metadata include one or more of name or an identifier.

7. The method of claim 1 , wherein the garbage collection data structure includes a comma separated value file.

8. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause at least one machine to perform operations comprising:

providing a garbage collection data structure, the garbage collection data structure including metadata and one or more resource consumption parameters corresponding to a container being executed on a host machine;

analyzing, by a machine-learning function, the one or more resource consumption parameters;

classifying, based on the analyzing, the container into one or more classes, the one or more classes including at least one of a suspicious container class, a malicious container class, or a normal container class; and

performing, based on the analyzing, one or more garbage collection actions on the container, including at least one of generating an alert corresponding to the container, reducing a resource consumption of the container, or marking the container as safe.

9. The non-transitory machine-readable medium of claim 8 , wherein the machine-learning function includes a decision tree, and wherein a first attribute of the decision tree includes a first one of the one or more resource consumption parameters, and a second attribute of the decision tree includes a second one of the one or more resource consumption parameters.

10. The non-transitory machine-readable medium of claim 8 , wherein the one or more garbage collection actions for reducing the resource consumption of the container includes stopping execution of the container.

11. The non-transitory machine-readable medium of claim 8 , wherein the one or more garbage collection actions for reducing the resource consumption of the container includes deleting the container.

12. The non-transitory machine-readable medium of claim 8 , wherein the one or more resource consumption parameters include a processor usage, a memory usage, or a duration corresponding to the container.

13. The non-transitory machine-readable medium of claim 8 , wherein the metadata include one or more of a name or an identifier.

14. The non-transitory machine-readable medium of claim 8 , wherein the garbage collection data structure includes a comma separated value file.

15. A system comprising:

a non-transitory memory; and

one or more hardware processors coupled to the non-transitory memory to execute instructions from the non-transitory memory to perform operations comprising:

providing a garbage collection data structure, the garbage collection data structure including metadata and a resource consumption parameter corresponding to a container being executed on a host machine;

analyzing, by a machine-learning function, the metadata and the resource consumption parameter;

based on the analyzing, assigning a class to the container; and

based on the assigning, performing a garbage collection action on the container.

16. The system of claim 15 , wherein the machine-learning function includes a decision tree, and wherein a first attribute of the decision tree includes the resource consumption parameter, and a second attribute of the decision tree includes another of the resource consumption parameter.

17. The system of claim 15 , wherein the garbage collection action includes generating an alert corresponding to the container, reducing a resource consumption of the container, or marking the container as safe.

18. The system of claim 15 , further comprising discretizing, by the machine-learning function, the resource consumption parameter.

19. The system of claim 15 , further comprising determining, based on the analyzing, that a new class should be created for the container image, and creating the new class.

20. The system of claim 15 , wherein the analyzing includes a comparison of the assigning the container class using different machine-learning functions and selecting a most accurate machine-learning function from among the different machine-learning functions.