Anomaly-based retention policies for snapshot-based data protection systems
One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.
1. A method, comprising:
monitoring performance of a computing system element of a runtime environment, and the monitoring comprises collecting performance information concerning the computing system element;
analyzing the collected information;
detecting, based on the analyzing of the collected information, an anomaly in the performance of the computing system element; and
in response to the detecting of the anomaly, automatically marking a snapshot of the computing system element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.
2. The method as recited in claim 1 , wherein the marking of the snapshot sets a retention period for the snapshot that is longer than a retention period set for the snapshot by the retention policy.
3. The method as recited in claim 1 , wherein the marking of the snapshot prevents deletion of the snapshot until the snapshot is unmarked.
4. The method as recited in claim 1 , wherein the snapshot is marked by a computing entity.
5. The method as recited in claim 1 , wherein the computing system element is one or more of an application, a virtual machine, a physical computing device, or a container.
6. The method as recited in claim 1 , wherein the monitoring of the performance of the computing system element comprises monitoring one or more key performance indicators relating to the performance of the computing system element.
7. The method as recited in claim 1 , wherein the analyzing of the collected information comprises using a time series model and a Gaussian Mixture Model (GMM) to analyze the collected information.
8. The method as recited in claim 1 , wherein the marking of the snapshot comprises connecting to a data protection system where the snapshot is stored.
9. The method as recited in claim 1 , further comprising unmarking a marked snapshot.
10. The method as recited in claim 1 , further comprising using a Gaussian Mixture Model to learn a normal state for the computing system element.
11. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
monitoring performance of a computing system element of a runtime environment, and the monitoring comprises collecting performance information concerning the computing system element;
analyzing the collected information;
detecting, based on the analyzing of the collected information, an anomaly in the performance of the computing system element; and
in response to the detecting of the anomaly, automatically marking a snapshot of the computing system element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.
12. The non-transitory storage medium as recited in claim 11 , wherein the marking of the snapshot sets a retention period for the snapshot that is longer than a retention period set for the snapshot by the retention policy.
13. The non-transitory storage medium as recited in claim 11 , wherein the marking of the snapshot prevents deletion of the snapshot until the snapshot is unmarked.
14. The non-transitory storage medium as recited in claim 11 , wherein the snapshot is marked by a computing entity.
15. The non-transitory storage medium as recited in claim 11 , wherein the computing system element is one or more of an application, a virtual machine, a physical computing device, or a container.
16. The non-transitory storage medium as recited in claim 11 , wherein the monitoring of the performance of the computing system element comprises monitoring one or more key performance indicators relating to the performance of the computing system element.
17. The non-transitory storage medium as recited in claim 11 , wherein the analyzing of the collected information comprises using a time series model and a Gaussian Mixture Model (GMM) to analyze the collected information.
18. The non-transitory storage medium as recited in claim 11 , wherein the marking of the snapshot comprises connecting to a data protection system where the snapshot is stored.
19. The non-transitory storage medium as recited in claim 11 , wherein the operations further comprise unmarking a marked snapshot.
20. The non-transitory storage medium as recited in claim 1 , wherein the operations further comprise using a Gaussian Mixture Model to learn a normal state for the computing system element.