Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

Abstract

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.