Periodic Subflow-based Proactive Flow InstallationMechanism in SDN-based IoT

  • 卓 景昭

Student thesis: Doctoral Thesis


With the developing of 5G mobile network 5G provides a new scenario and new direction of development for the industry making the Internet of Things (IoT) into the golden age of development With the developing of IoT the number of IoT devices in the network keep increasing According to the report of McKinsey Global Institute which released in 2015 there will be 1 trillion IoT devices connect to the internet SDN is necessary since ISPs require to provide various services for various IoT devices However the size of the flow table is limited and it can not accommodate all flow entries for all traffic passing through Thus an efficient flow entry management scheme is required to reduce the processing delay and signal overhead In general network delayed installation is proposed and it may reduce the number of flow entries However it will increase the processing delay downgrading the network performance PFIM and EPFIM are designed for IoT They reduce the processing delay by detecting the periodicity of traffic and pre-installing flow entries However they are not able to detect multiple periodicities Besides there is still room for improving the accuracy of periodic detection and reducing signal overhead In this thesis we propose a periodic subflow-based proactive flow installation mechanism We design a new method of data collection to improve the accuracy of pre-installation A new data structure is designed to enable the detection of multiple periodicities in a single flow A new algorithm is proposed to achieve accurate periodic calculation Compared with the previous mechanism our mechanism improves the hit ratio of flow table hit by up to 80% and improves the required installation per table hit up to 99%
Date of Award2020
Original languageEnglish
SupervisorMeng-Hsun Tsai (Supervisor)

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