Prioritization and prediction of jobs using cognitive rules engine

1. A method for job prioritization, the method comprising:

receiving, by a cognitive rules engine, one or more jobs;

analyzing, using a learned metric of the cognitive rules engine, the one or more jobs, wherein the learned metric is based on a user inputted priority of the one or more jobs, a number of consumers of a result of the one or more jobs, a dependency of the one or more jobs, a number of computational resources required to run the one or more jobs, wherein the learned metric of the cognitive rules engine comprises:

using a result consumption analysis module to predict the number of consumers of the result of the one or more jobs;

using a dependency analysis module to determine the dependency of the one or more jobs on each other;

using an estimation of job start time module to determine a time required to finish a jobs in order to meet a service level agreement (SLA);

using a resource consumption analysis module to determine the number of computational resources required to run the one or more jobs;

using a user consumption analysis module to determine a consumer of the result of the one or more jobs; and

using a historical priority module to determine a historical priority of the one or more jobs or of a job with same characteristics; and

prioritizing the one or more jobs based on a result of the learned metric of the cognitive rules engine.

2. The method of claim 1 , wherein the one or more jobs feeds into a data repository.

3. The method of claim 1 , wherein, based on the dependency analysis module, a priority of the one or more jobs increases when a result of the one or more jobs is used by another job with a higher priority.

4. The method of claim 1 , wherein, based on the estimation of job start time module, a priority of the one or more jobs increases when triggering the one or more jobs will meet a service level agreement (SLA).

5. The method of claim 1 , further comprising:

predicting an availability of an executing server, based on learned metrics which are results of the resource consumption analysis module and the estimation of job start time module.

6. The method of claim 5 , further comprising:

generating a predictive model for job prioritization based on a modified value of the learned metric of the cognitive rules engine.

7. A computer system for job prioritization, comprising:

one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system performs a method comprising:

receiving, by a cognitive rules engine, one or more jobs;

analyzing, using a learned metric of the cognitive rules engine, the one or more jobs, wherein the learned metric is based on a user inputted priority of the one or more jobs, a number of consumers of a result of the one or more jobs, a dependency of the one or more jobs, a number of computational resources required to run the one or more jobs, wherein the learned metric of the cognitive rules engine comprises:

using a result consumption analysis module to predict the number of consumers of the result of the one or more jobs;

using a dependency analysis module to determine the dependency of the one or more jobs on each other;

using an estimation of job start time module to determine a time required to finish a jobs in order to meet a service level agreement (SLA);

using a resource consumption analysis module to determine the number of computational resources required to run the one or more jobs;

using a user consumption analysis module to determine a consumer of the result of the one or more jobs; and

using a historical priority module to determine a historical priority of the one or more jobs or of a job with same characteristics; and

prioritizing the one or more jobs based on a result of the learned metric of the cognitive rules engine.

8. The computer system of claim 7 , wherein the one or more jobs feeds into a data repository.

9. The computer system of claim 7 , wherein, based on the dependency analysis module, a priority of the one or more jobs increases when a result of the one or more jobs is used by another job with a higher priority.

10. The computer system of claim 7 , wherein, based on the estimation of job start time module, a priority of the one or more jobs increases when triggering the one or more jobs will meet a service level agreement (SLA).

11. The computer system of claim 7 , further comprising:

predicting an availability of an executing server, based on learned metrics which are results of the resource consumption analysis module and the estimation of job start time module.

12. The computer system of claim 11 , further comprising:

generating a predictive model for job prioritization based on a modified value of the learned metric of the cognitive rules engine.

13. A computer program product for job prioritization, comprising:

one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:

receiving, by a cognitive rules engine, one or more jobs;

analyzing, using a learned metric of the cognitive rules engine, the one or more jobs, wherein the learned metric is based on a user inputted priority of the one or more jobs, a number of consumers of a result of the one or more jobs, a dependency of the one or more jobs, a number of computational resources required to run the one or more jobs, wherein the learned metric of the cognitive rules engine comprises:

using a result consumption analysis module to predict the number of consumers of the result of the one or more jobs;

using a dependency analysis module to determine the dependency of the one or more jobs on each other;

using an estimation of job start time module to determine a time required to finish a jobs in order to meet a service level agreement (SLA);

using a resource consumption analysis module to determine the number of computational resources required to run the one or more jobs;

using a user consumption analysis module to determine a consumer of the result of the one or more jobs; and

using a historical priority module to determine a historical priority of the one or more jobs or of a job with same characteristics; and

prioritizing the one or more jobs based on a result of the learned metric of the cognitive rules engine.

14. The computer program product of claim 13 , wherein the one or more jobs feeds into a data repository.

15. The computer program product of claim 13 , wherein, based on the dependency analysis module, a priority of the one or more jobs increases when a result of the one or more jobs is used by another job with a higher priority.

16. The computer program product of claim 13 , further comprising:

predicting an availability of an executing server, based on learned metrics which are results of the resource consumption analysis module and the estimation of job start time module.

17. The computer program product of claim 16 , further comprising:

generating a predictive model for job prioritization based on a modified value of the learned metric of the cognitive rules engine.