View source for 18047459. GUIDED WORKLOAD PLACEMENT REINFORCEMENT LEARNING EXPERIENCE PRUNING USING RESTRICTED BOLTZMANN MACHINES simplified abstract (Dell Products L.P.)

=GUIDED WORKLOAD PLACEMENT REINFORCEMENT LEARNING EXPERIENCE PRUNING USING RESTRICTED BOLTZMANN MACHINES=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:Yanexis Pupo Toledo of Niteroi (BR)|Yanexis Pupo Toledo of Niteroi (BR)]][[Category:Yanexis Pupo Toledo of Niteroi (BR)]]

[[:Category:Micael Ver�ssimo De Ara�jo of Rio de Janeiro (BR)|Micael Ver�ssimo De Ara�jo of Rio de Janeiro (BR)]][[Category:Micael Ver�ssimo De Ara�jo of Rio de Janeiro (BR)]]

[[:Category:Eduardo Vera Sousa of Niteroi (BR)|Eduardo Vera Sousa of Niteroi (BR)]][[Category:Eduardo Vera Sousa of Niteroi (BR)]]

==GUIDED WORKLOAD PLACEMENT REINFORCEMENT LEARNING EXPERIENCE PRUNING USING RESTRICTED BOLTZMANN MACHINES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18047459 titled 'GUIDED WORKLOAD PLACEMENT REINFORCEMENT LEARNING EXPERIENCE PRUNING USING RESTRICTED BOLTZMANN MACHINES

==Simplified Explanation==

The abstract describes a method for analyzing workloads using machine learning models to determine which microservices should be used to execute the workload efficiently.

* The experiences for a workload are defined, along with the microservices associated with executing the workload.
* A probability of using each microservice is generated to execute the workload.
* Experiences with a low probability of generating a high reward are removed from analysis.
* The remaining experiences are analyzed to determine the optimal microservices for executing the workload.

== Potential Applications ==
This technology could be applied in cloud computing environments to optimize resource allocation and workload execution.

== Problems Solved ==
This technology helps in efficiently assigning microservices to execute workloads, improving overall system performance and resource utilization.

== Benefits ==
The method allows for automated and optimized workload analysis, leading to cost savings and improved efficiency in computing environments.

== Potential Commercial Applications ==
"Optimizing Microservice Workload Execution in Cloud Computing Environments"

== Possible Prior Art ==
There may be prior art related to workload optimization in cloud computing environments using machine learning models.

== Unanswered Questions ==

=== How does this method handle dynamic workloads that change over time? ===
The method does not address how it adapts to changing workloads or if it requires manual intervention for updates.

=== What impact does the removal of experiences with low reward probabilities have on overall system performance? ===
The abstract does not mention the potential consequences of removing certain experiences on the efficiency of workload execution.


==Original Abstract Submitted==

One example method includes defining experiences for a workload that are to be analyzed at a first machine-learning (ML) model. The experiences define an association between the workload and microservices having computing resources that execute the workload. A probability of using each of the microservices of the experiences to execute the workload is generated at a second ML mode. A determination is made of which of the experiences have a probability that indicates that the experience will generate a low reward when analyzed by the first ML model. The experiences that generate the low reward are removed from the experiences to be analyzed at the first ML model. The experiences that have not been removed are analyzed at the first ML model to determine which experience includes microservices that should be used to execute the workload.



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