View source for International business machines corporation (20240103896). INTELLIGENTLY SCALING DATABASE AS A SERVICE RESOURCES IN A CLOUD PLATFORM simplified abstract

=INTELLIGENTLY SCALING DATABASE AS A SERVICE RESOURCES IN A CLOUD PLATFORM=


==Organization Name==

[[:Category:international business machines corporation|international business machines corporation]]


[[Category:international business machines corporation]]

==Inventor(s)==

[[:Category:Peng Hui Jiang of Beijing (CN)|Peng Hui Jiang of Beijing (CN)]][[Category:Peng Hui Jiang of Beijing (CN)]]

[[:Category:Yue Wang of Beijing (CN)|Yue Wang of Beijing (CN)]][[Category:Yue Wang of Beijing (CN)]]

[[:Category:Jun Su of Beijing (CN)|Jun Su of Beijing (CN)]][[Category:Jun Su of Beijing (CN)]]

[[:Category:Su Liu of Austin TX (US)|Su Liu of Austin TX (US)]][[Category:Su Liu of Austin TX (US)]]

[[:Category:Sheng Yan Sun of Beijing (CN)|Sheng Yan Sun of Beijing (CN)]][[Category:Sheng Yan Sun of Beijing (CN)]]

==INTELLIGENTLY SCALING DATABASE AS A SERVICE RESOURCES IN A CLOUD PLATFORM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240103896 titled 'INTELLIGENTLY SCALING DATABASE AS A SERVICE RESOURCES IN A CLOUD PLATFORM

==Simplified Explanation==

The abstract describes a method, system, and computer program product for scaling a resource of a Database as a Service (DBaaS) cluster in a cloud platform. Tracing data is generated from user service requests, and a dependency tree is created to identify potential bottlenecks in nodes of the DBaaS cluster, allowing for the scaling of resources based on this information.

* Tracing data is generated from user service requests and handling of these requests by the DBaaS cluster.
* A dependency tree is created to identify potential bottlenecks in nodes of the DBaaS cluster.
* The scaling of resources is based on the dependency tree to predict and address potential bottlenecks in the DBaaS cluster.

== Potential Applications ==
The technology described in this patent application could be applied in cloud platforms offering Database as a Service (DBaaS) to efficiently scale resources based on user service requests and potential bottlenecks in the system.

== Problems Solved ==
1. Efficient scaling of resources in a DBaaS cluster based on user service requests.
2. Identification and prediction of potential bottlenecks in the DBaaS cluster to optimize performance.

== Benefits ==
1. Improved performance and resource utilization in a DBaaS cluster.
2. Automated scaling based on real-time data and dependencies in the system.

== Potential Commercial Applications ==
Optimizing resource scaling in DBaaS clusters for improved performance and efficiency in cloud platforms.

== Possible Prior Art ==
There may be prior art related to scaling resources in cloud platforms or databases based on real-time data and dependencies to optimize performance.

=== What are the potential challenges in implementing this technology in real-world scenarios? ===

Implementing this technology in real-world scenarios may face challenges such as:
1. Ensuring the accuracy and reliability of the dependency tree in identifying bottlenecks.
2. Integrating the scaling mechanism seamlessly with existing DBaaS clusters and cloud platforms.

=== How does this technology compare to traditional methods of scaling resources in a DBaaS cluster? ===

Compared to traditional methods, this technology offers a more data-driven and automated approach to scaling resources in a DBaaS cluster, based on real-time tracing data and dependency analysis.


==Original Abstract Submitted==

a computer-implemented method, system and computer program product for scaling a resource of a database as a service (dbaas) cluster in a cloud platform. user service requests from a service cluster to be processed by the dbaas cluster are received. a first set of tracing data is generated by a service mesh, which facilitates service-to-service communication between the service cluster and the dbaas cluster, from the user service requests. a second set of tracing data is generated by the dbaas cluster from handling the user service requests. a dependency tree is then generated to discover application relationships to identify potential bottlenecks in nodes of the dbaas cluster based on these sets of tracing data. the pod(s) of a dbaas node are then scaled based on the dependency tree, which is used in part, to predict the utilization of the resources of the dbaas node identified as being a potential bottleneck.



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