Adaptive Makeup Artist Optimization Based Hierarchical Scale Convolutional Neural Network for 5G Network Slicing
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I4P113Keywords:
5G network, network slicing, network parameters, Hierarchical Scale Convolutional Neural Network, Makeup Artist Optimization AlgorithmAbstract
Fifth-Generation (5G) network slicing offers Network-as-a-Service (NaaS) for various use cases, permitting network operators to construct numerous virtual networks on distributed infrastructure. With a network slicing, the service providers can deploy their services and applications quickly as well as flexibly for accommodating certain requirements of various services. As a developing technology with many benefits, the network slicing has increased many problems for academia and industry. Some existing slicing techniques lack in capability to dynamically adapt to varying network conditions, decreasing their efficacy in highly changeable scenarios. In this research, Adaptive Makeup Artist Optimization based Hierarchical Scale Convolutional Neural Network (AMAO_HSNet) is introduced for the 5G network slicing. Initially, system model of secure 5G network is simulated. Whenever the network slicing requests arrive from User Equipment (UE), the set of parameters, namely delay rate, packet loss rate, device type and speed, are collected from different devices for network slicing. Lastly, network slicing is performed employing Hierarchical Scale Convolutional Neural Network (HSNet). The training process of HSNet is done using Adaptive Makeup Artist Optimization (AMAO). However, AMAO is derived by incorporating adaptive concept with Makeup Artist Optimization Algorithm (MAOA). The services provided by 5G network can be accessed by an Internet Service Provider (ISP) utilizing Virtual Network Function (VNF). Additionally, AMAO_HSNet has obtained high acceptance rate and resource efficiency of 0.910 and as well as low execution time of 0.172ssec
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