US Patent Application 18042288. METHOD AND APPARATUS FOR AUTOSCALING CONTAINERS IN A CLOUD-NATIVE CORE NETWORK simplified abstract
Contents
METHOD AND APPARATUS FOR AUTOSCALING CONTAINERS IN A CLOUD-NATIVE CORE NETWORK
Inventors
METHOD AND APPARATUS FOR AUTOSCALING CONTAINERS IN A CLOUD-NATIVE CORE NETWORK - A simplified explanation of the abstract
- This abstract for appeared for patent application number 18042288 Titled 'METHOD AND APPARATUS FOR AUTOSCALING CONTAINERS IN A CLOUD-NATIVE CORE NETWORK'
Simplified Explanation
This abstract describes a communication method and system that combines 5G technology with Internet of Things (IoT) technology. It focuses on supporting higher data rates and enabling intelligent services such as smart home, smart city, and connected car applications. The abstract also introduces a method that uses artificial intelligence (AI) to automatically adjust the number of containers in a cloud-native core network based on metrics obtained from a metrics server. This autoscaling decision is made using a trained machine learning model.
Original Abstract Submitted
The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A method performed by an artificial intelligence (AI) module for autoscaling containers of a cloud-native core network with containerised network functions is provided. The method comprising requesting, from at least one metrics server, at least one metric required to make an autoscaling decision with respect to at least one set of containers; receiving the at least one metric from the at least one metrics server; processing the received at least one metric, using a trained machine learning (ML) model, to make an autoscaling decision with respect to the set of containers; and implementing the autoscaling decision with respect to the set of containers.
