Open AccessAdmission Control and Resource Allocation in 5G Network Slicing
Author(s) -
William F. Villota Jácome,
Óscar Mauricio Caicedo Rendón,
Nelson L. S. da Fonseca
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbrc_estendido.2021.17158
Subject(s) - slicing , computer science , quality of service , resource allocation , admission control , reinforcement learning , computer network , resource management (computing) , profit (economics) , resource (disambiguation) , control (management) , distributed computing , operations research , artificial intelligence , world wide web , microeconomics , engineering , economics
This paper summarizes the research in the master thesis entitled "Admission Control and Resource Allocation in 5G Network Slicing". We propose two solutions, SARA and DSARA, based on Reinforcement Learning algorithms to learn the admission policy that optimizes the profit of providers. Resource allocation considers the QoS requirements. Results show the outstanding performance of our solutions to 5G Network Slicing in relation to profit and resource utilization.