Data center monitoring and management operation including data center alert prioritization by shaping rewards

1. A computer-implementable method for performing a data center management and monitoring operation, comprising:

receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data;

assigning the data center data to a vectorized input space;

reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension;

decoding the latent space to provide a vectorized decoded output space;

performing a data center asset operational status forecasting operation using the vectorized decoded output space, the data center asset operational status forecasting operation generating data center asset operational status forecasting data;

performing a time estimation operation, the time estimation operation using the data center asset operational status forecasting data; and,

prioritizing data center asset remediation operations using the data center asset operational status forecasting data; and wherein

the prioritizing uses a reward shaping operation when prioritizing the data center asset remediation operations;

the reward shaping operation is used to train a policy; and,

the policy is trained using one of semi supervised learning and unsupervised learning.

2. The method of claim 1 , wherein:

the reward shaping operation uses rules and states when prioritizing the data center asset remediation operations.

3. The method of claim 2 , wherein:

the reward shaping operation determines a value of a reward;

the reward includes at least one of a service factor reward and a criticality reward.

4. The method of claim 1 , wherein:

the policy training is adaptive.

5. A system comprising:

a processor;

a data bus coupled to the processor;

a data center asset client module; and,

a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:

receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data;

assigning the data center data to a vectorized input space;

reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension;

decoding the latent space to provide a vectorized decoded output space;

performing a data center asset operational status forecasting operation using the vectorized decoded output space, the data center asset operational status forecasting operation generating data center asset operational status forecasting data;

performing a time estimation operation, the time estimation operation using the data center asset operational status forecasting data; and,

prioritizing data center asset remediation operations using the data center asset operational status forecasting data; and wherein

the prioritizing uses a reward shaping operation when prioritizing the data center asset remediation operations;

the reward shaping operation is used to train a policy; and,

the policy is trained using one of semi supervised learning and unsupervised learning.

6. The system of claim 5 , wherein:

the reward shaping operation uses rules and states when prioritizing the data center asset remediation operations.

7. The system of claim 6 , wherein:

the reward shaping operation determines a value of a reward;

the reward includes at least one of a service factor reward and a criticality reward.

8. The system of claim 5 , wherein:

the policy training is adaptive.

9. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

receiving data center data from a plurality of data center assets within a data center, the data center data comprising data center asset data;

assigning the data center data to a vectorized input space;

reducing a dimension of the vectorized input space to a latent space, the latent space providing an operational status analysis (OSA) model dimension;

decoding the latent space to provide a vectorized decoded output space;

performing a data center asset operational status forecasting operation using the vectorized decoded output space, the data center asset operational status forecasting operation generating data center asset operational status forecasting data;

performing a time estimation operation, the time estimation operation using the data center asset operational status forecasting data; and,

prioritizing data center asset remediation operations using the data center asset operational status forecasting data; and wherein

the prioritizing uses a reward shaping operation when prioritizing the data center asset remediation operations;

the reward shaping operation is used to train a policy; and,

the policy is trained using one of semi supervised learning and unsupervised learning.

10. The non-transitory, computer-readable storage medium of claim 9 , wherein:

the reward shaping operation uses rules and states when prioritizing the data center asset remediation operations.

11. The non-transitory, computer-readable storage medium of claim 10 , wherein:

the reward shaping operation determines a value of a reward;

the reward includes at least one of a service factor reward and a criticality reward.

12. The non-transitory, computer-readable storage medium of claim 9 , wherein:

the policy training is adaptive.

13. The non-transitory, computer-readable storage medium of claim 9 , wherein:

the computer executable instructions are deployable to a client system from a server system at a remote location.

14. The non-transitory, computer-readable storage medium of claim 9 , wherein:

the computer executable instructions are provided by a service provider to a user on an on-demand basis.