International business machines corporation (20240113945). CONTINUOUSLY IMPROVING API SERVICE ENDPOINT SELECTIONS VIA ADAPTIVE REINFORCEMENT LEARNING simplified abstract
Contents
- 1 CONTINUOUSLY IMPROVING API SERVICE ENDPOINT SELECTIONS VIA ADAPTIVE REINFORCEMENT LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTINUOUSLY IMPROVING API SERVICE ENDPOINT SELECTIONS VIA ADAPTIVE REINFORCEMENT LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
CONTINUOUSLY IMPROVING API SERVICE ENDPOINT SELECTIONS VIA ADAPTIVE REINFORCEMENT LEARNING
Organization Name
international business machines corporation
Inventor(s)
Rong Nickle Chang of Pleasantville NY (US)
Nitin Gaur of Round Rock TX (US)
CONTINUOUSLY IMPROVING API SERVICE ENDPOINT SELECTIONS VIA ADAPTIVE REINFORCEMENT LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240113945 titled 'CONTINUOUSLY IMPROVING API SERVICE ENDPOINT SELECTIONS VIA ADAPTIVE REINFORCEMENT LEARNING
Simplified Explanation
The method described in the abstract involves using a machine learning model to select an endpoint for a web-based service based on a feature vector generated from the parameters of a request.
- The processor set receives a request for a web-based service.
- The processor set generates a feature vector based on the parameters of the request.
- The processor set generates an endpoint selection vector with probabilities for different endpoints using the feature vector and a machine learning model.
- One of the endpoints is selected based on the probabilities in the selection vector.
- The selected endpoint is invoked by the processor set.
Potential Applications
This technology could be applied in various industries such as e-commerce, online services, and cloud computing to optimize endpoint selection for web-based services.
Problems Solved
This technology solves the problem of efficiently selecting the best endpoint for a web-based service based on the parameters of a request, improving overall performance and user experience.
Benefits
The benefits of this technology include improved efficiency in endpoint selection, enhanced user experience, and optimized resource utilization for web-based services.
Potential Commercial Applications
A potential commercial application of this technology could be in cloud service providers to optimize endpoint selection for different clients, improving overall service performance.
Possible Prior Art
One possible prior art could be the use of machine learning models for endpoint selection in cloud computing or network routing algorithms.
Unanswered Questions
How does this technology handle dynamic changes in endpoint performance or availability?
The article does not address how the system adapts to changes in endpoint performance or availability over time. This could be crucial for maintaining optimal service delivery.
What kind of machine learning model is used for generating the endpoint selection vector?
The article does not specify the type of machine learning model used for generating the endpoint selection vector. Understanding the model could provide insights into the accuracy and efficiency of the endpoint selection process.
Original Abstract Submitted
a method includes: receiving, by a processor set, a request for a web-based service; generating, by the processor set, a feature vector including values based on parameters of the request; generating, by the processor set, an endpoint selection vector including plural probabilities corresponding to plural endpoints, wherein the endpoint selection vector is generated using the feature vector with a machine learning model; selecting, by the processor set, one of the plural endpoints based on the plural probabilities; and invoking, by the processor set, the selected endpoint.
