Intelligent backup and restoration of containerized environment
A system for determining the optimal backup and restore location for lightweight applications is provided. A computer device identifies a set of training data, wherein the training data identifies a lightweight container, a corresponding backup lightweight container, and an optimization score for the lightweight container and the corresponding backup lightweight container. The computing device trains a machine learning model utilizing the identified training data. The computing device identifies a new lightweight container for backup. The computing device determines an optimal backup lightweight container for the new lightweight container utilizing the trained machine learning model.
1. A computer-implemented method, the method comprising:
identifying, by one or more processors, a set of training data, wherein the training data identifies a lightweight container, a corresponding backup lightweight container, and an optimization score for the lightweight container and the corresponding backup lightweight container;
training, by the one or more processors, an intelligent containerization machine learning model utilizing the identified training data;
identifying, by the one or more processors, utilizing the intelligent containerization machine learning model, a new lightweight container for backing up, the new lightweight container being communicatively connected to a storage device;
identifying, by the one or more processors, available backup lightweight containers; and
determining, by the one or more processors, an optimal backup lightweight container for the new lightweight container utilizing the trained intelligent containerization machine learning model, the optimal backup lightweight container being a respective one of the available backup lightweight containers that, when paired with the new lightweight container as an input to the trained intelligent containerization machine learning model, results in a highest respective optimization score,
wherein:
the training data further identifies: (i) unstructured container data for the lightweight container, (ii) configuration data for the lightweight container, and (iii) backup data for the backup lightweight container; and
the new lightweight container includes corresponding unstructured container data and configuration data.
2. The computer-implemented method of claim 1 , wherein:
the unstructured container data includes text data for the lightweight container, wherein the text data includes at least a container name and log data;
the configuration data includes hardware and software requirements for the lightweight container; and
the backup data includes hardware and software features for the backup lightweight container.
3. The computer-implemented method of claim 1 , wherein the determined optimal backup lightweight container runs on a plurality of nodes in a container environment, wherein the plurality of nodes includes a master node and one or more worker nodes communicatively connected to the storage device for backup and restore of data stored on the lightweight applications.
4. The computer-implemented method of claim 1 , the method further comprising:
identifying, by the one or more processors, one or more patterns from the unstructured container data for the lightweight container; and
utilizing, by the one or more processors, the identified one or more patterns in training the intelligent containerization machine learning model,
wherein the determined optimal backup lightweight container for the new lightweight container is determined based, at least in part, on respective patterns from the unstructured container data of the new lightweight container.
5. The computer-implemented method of claim 4 , wherein the identifying of the one or more patterns utilizes a K-mean algorithm.
6. The computer-implemented method of claim 5 , further comprising:
generating, by the one or more processors, the optimization score for the lightweight container and the corresponding backup lightweight container, wherein the optimization score is based, at least in part, on the intelligent backup and restore algorithm considering: (i) a result of the K-mean algorithm, (ii) a latency constraint, and (iii) a required backup period of time.
7. A computer program product comprising:
one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the stored program instructions comprising:
program instructions to identify a set of training data, wherein the training data identifies a lightweight container, a corresponding backup lightweight container, and an optimization score for the lightweight container and the corresponding backup lightweight container;
program instructions to train an intelligent containerization machine learning model utilizing the identified training data;
program instructions to identify, utilizing the intelligent containerization machine learning model, a new lightweight container for backing up, the new lightweight container being communicatively connected to a storage device;
program instructions to identify available backup lightweight containers; and
program instructions to determine an optimal backup lightweight container for the new lightweight container utilizing the trained intelligent containerization machine learning model, the optimal backup lightweight container being a respective one of the available backup lightweight containers that, when paired with the new lightweight container as an input to the trained intelligent containerization machine learning model, results in a highest respective optimization score,
wherein:
the training data further identifies: (i) unstructured container data for the lightweight container, (ii) configuration data for the lightweight container, and (iii) backup data for the backup lightweight container; and
the new lightweight container includes corresponding unstructured container data and configuration data.
8. The computer program product of claim 7 , wherein:
the unstructured container data includes text data for the lightweight container, wherein the text data includes at least a container name and log data;
the configuration data includes hardware and software requirements for the lightweight container; and
the backup data includes hardware and software features for the backup lightweight container.
9. The computer program product of claim 7 , wherein the determined optimal backup lightweight container runs on a plurality of nodes in a container environment, wherein the plurality of nodes includes a master node and one or more worker nodes.
10. The computer program product of claim 7 , the stored program instructions further comprising:
program instructions to identify one or more patterns from the unstructured container data for the lightweight container; and
program instructions to include the identified one or more patterns in training the intelligent containerization machine learning model,
wherein the determined optimal backup lightweight container for the new lightweight container is determined based, at least in part, on respective patterns from the unstructured container data of the new lightweight container.
11. The computer program product of claim 10 , wherein the identifying of the one or more patterns utilizes a K-mean algorithm.
12. The computer program product of claim 11 , the stored program instructions further comprising:
program instructions to determine the optimization score for the lightweight container and the corresponding backup lightweight container, wherein the optimization score is based, at least in part, on: (i) a result of the K-mean algorithm, (ii) a latency constraint, and (iii) a required backup period of time.
13. A computer system, the computer system comprising:
one or more computer processors:
one or more computer readable storage medium; and
program instructions stored on the computer readable storage medium for execution by at least one of the one or more processors, the stored program instructions comprising:
program instructions to identify a set of training data, wherein the training data identifies a lightweight container, a corresponding backup lightweight container, and an optimization score for the lightweight container and the corresponding backup lightweight container;
program instructions to train an intelligent containerization machine learning model utilizing the identified training data;
program instructions to identify, utilizing the intelligent containerization machine learning model, a new lightweight container for backing up, the new lightweight container being communicatively connected to a storage device;
program instructions to identify available backup lightweight containers; and
program instructions to determine an optimal backup lightweight container for the new lightweight container utilizing the trained intelligent containerization machine learning model, the optimal backup lightweight container being a respective one of the available backup lightweight containers that, when paired with the new lightweight container as an input to the trained intelligent containerization machine learning model, results in a highest respective optimization score,
wherein:
the training data further identifies: (i) unstructured container data for the lightweight container, (ii) configuration data for the lightweight container, and iii) backup data for the backup lightweight container; and
the new lightweight container includes corresponding unstructured container data and configuration data.
14. The computer system of claim 13 , wherein:
the unstructured container data includes text data for the lightweight container, wherein the text data includes at least a container name and log data;
the configuration data includes hardware and software requirements for the lightweight container; and
the backup data includes hardware and software features for the backup lightweight container.
15. The computer system of claim 13 , the stored program instructions further comprising:
program instructions to identify one or more patterns from the unstructured container data for the lightweight container; and
program instructions to include the identified one or more patterns in training the intelligent containerization machine learning model,
wherein the determined optimal backup lightweight container for the new lightweight container is determined based, at least in part, on respective patterns from the unstructured container data of the new lightweight container.
16. The computer system of claim 15 , wherein the identifying of the one or more patterns utilizes a K-mean algorithm.
17. The computer system of claim 16 , the stored program instructions further comprising:
determining, by one or more processors, the optimization score for the lightweight container and the corresponding backup lightweight container, wherein the optimization score is based, at least in part, on: (i) a result of the K-mean algorithm, (ii) a latency constraint, and (iii) a required backup period of time.