Publication: The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed
Date
2023
Authors
Ridwan M.A.
Radzi N.A.M.
Azmi K.H.M.
Ahmad A.
Abdullah F.
Ahmad W.S.H.M.W.
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Computer Society
Abstract
High quality of service (QoS) requires monitoring and controlling parameters such as delay and throughput. Due to network complexity, conventional QoS-improving routing algorithms (RAs) may be impractical. Thus, researchers are developing intelligent RAs, including machine learning (ML)-based algorithms to meet traffic Q oS r equirements. However, most current studies evaluate performance using simulations. Validation requires real-world environment studies, but lab-scale testbed studies are limited. Therefore, we proposed an ML-based RA (ML-RA-t) to improve delay and throughput, evaluated using simulation and a lab-scale testbed. The results show that ML-RA-t predicted the fastest route as compared to RIPv2 routing protocol in simulation and testbed. � 2023 IEEE.
Description
Keywords
machine learning , QoS , routing algorithm , simulation , testbed , Learning algorithms , Machine learning , Quality control , Quality of service , Testbeds , Controlling parameters , High quality , Intelligent routing algorithm , Learning-based algorithms , Machine-learning , Monitoring and controlling , Monitoring parameters , Network complexity , Quality-of-service , Simulation , Routing algorithms