View source for 18391626. Predicting Application Performance From Resource Statistics simplified abstract (Oracle International Corporation)

=Predicting Application Performance From Resource Statistics=


==Organization Name==

[[:Category:Oracle International Corporation|Oracle International Corporation]]


[[Category:Oracle International Corporation]]

==Inventor(s)==

[[:Category:Philip Eugene Cannata of Austin TX (US)|Philip Eugene Cannata of Austin TX (US)]][[Category:Philip Eugene Cannata of Austin TX (US)]]

==Predicting Application Performance From Resource Statistics - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18391626 titled 'Predicting Application Performance From Resource Statistics

==Simplified Explanation==

The abstract describes a system and method for generating a data throughput estimation model by monitoring a system to measure data throughput and computing statistics of computing resources. The relationship between these factors is used to create a model using machine learning algorithms.

* Data throughput estimation model generated by monitoring system
* Initial data set created by measuring data throughput and computing statistics
* Machine learning model, neural network algorithm, boosting decision tree algorithm, and/or random forest decision tree algorithm used to generate the estimation model
* Additional computing resource statistics applied to the model for improved accuracy

== Potential Applications ==
This technology could be applied in various industries such as telecommunications, cloud computing, and network optimization to predict data throughput and optimize resource allocation.

== Problems Solved ==
This technology helps in accurately estimating data throughput, which can aid in resource planning, capacity management, and performance optimization in complex computing systems.

== Benefits ==
- Improved efficiency in resource allocation
- Enhanced performance optimization
- Better capacity management

== Potential Commercial Applications ==
- Telecommunications companies
- Cloud service providers
- Network infrastructure companies

== Possible Prior Art ==
One possible prior art could be the use of traditional statistical methods for estimating data throughput in computing systems.

=== Unanswered Questions ===

=== How does this technology compare to existing data throughput estimation models in terms of accuracy and efficiency? ===
This article does not provide a direct comparison with existing models, leaving the reader to wonder about the performance of this technology in relation to others.

=== What specific industries or use cases could benefit the most from implementing this data throughput estimation model? ===
While the article mentions potential applications in various industries, it does not delve into specific use cases or industries that could see the most significant impact from this technology.


==Original Abstract Submitted==

Embodiments include systems and methods for generating a data throughput estimation model. A system may be monitored to measure both (a) data throughput and (b) computing statistics of one or more computing resources to generate an initial data set. The relationship between the data throughput and the computing statistics, in the initial data set, is used to generate a data throughput estimation model. The data throughput estimation model may be generated using a machine learning model, a neural network algorithm, boosting decision tree algorithm, and/or a random forest decision tree algorithm. Additional measurements of the computing resource statistics may be applied to the data throughput estimation model to estimate data throughput.



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