Learning-Based Framework for Resource Allocation for Varying Traffic

Abstract

AbstractNetwork function virtualization (NFV) presents a model to remove physical middle-boxes and replace them with virtual network functions (VNFs) that are more resilient. Adjusting resource allocation in response to the varying demands of traffic, there is a need for instantiating the VNFs and also balancing the resource allocation based on demand. Current optimization methods frequently expect the amount of resources required by every VNFs instance is fixed, resulting in either resource wastage or poor quality of service. To resolve this issue, machine learning (ML) models are used on real-time data of VNF, which contains performance indicators and requirement of resources. Evaluating the result, using ML models along with the VNF placement algorithms shows the reduced amount of resource consumption, thereby improving the quality of service and reducing the delay.KeywordsResource allocationMachine learningVNFQoS