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

international business machines corporation

Inventor(s)

Peng Hui Jiang of Beijing (CN)

Yue Wang of Beijing (CN)

Jun Su of Beijing (CN)

Su Liu of Austin TX (US)

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.