An intelligent method for reducing the overhead of analysing big data flows in Openflow switch

Abstract Software‐defined networks have been developed to allow the entire network to be managed as a programmable entity. As a well‐known protocol in this field, OpenFlow installs new packet forwarding rules of the distinct packets of Big Data flows (known as flow entries) in the flow tables of net...

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Main Authors: Mahdi Abbasi, Shima Maleki, Gwanggil Jeon, Mohammad R. Khosravi, Hatam Abdoli
Format: Article
Language:English
Published: Wiley 2022-03-01
Series:IET Communications
Online Access:https://doi.org/10.1049/cmu2.12328
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author Mahdi Abbasi
Shima Maleki
Gwanggil Jeon
Mohammad R. Khosravi
Hatam Abdoli
author_facet Mahdi Abbasi
Shima Maleki
Gwanggil Jeon
Mohammad R. Khosravi
Hatam Abdoli
author_sort Mahdi Abbasi
collection DOAJ
description Abstract Software‐defined networks have been developed to allow the entire network to be managed as a programmable entity. As a well‐known protocol in this field, OpenFlow installs new packet forwarding rules of the distinct packets of Big Data flows (known as flow entries) in the flow tables of network switches in order to implement the desired management policies. Despite the high speed, flow tables have limited capacity to store the information of Big Data flows. As a result of inefficient policy for replacing the entries of the flow table, lack of flow entries corresponding to the incoming packets in the flow table of the switch will increase the references to the controller for forwarding this packet as well as the amount of delay in packet forwarding. The underlying idea of the proposed method is to make use of the popularity of traffic flows in the table to select the intended flow for the replacement. For replacement of flow table entries, a novel and intelligent method is proposed in this research which uses a reference history of flows to assign an importance degree to each table entry. Comparison of the simulation results confirms the superiority of the method for reducing the controller's overflow.
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spelling doaj-art-e3ace4100327432daeb7e194e7404e9a2025-08-20T02:13:58ZengWileyIET Communications1751-86281751-86362022-03-0116554855910.1049/cmu2.12328An intelligent method for reducing the overhead of analysing big data flows in Openflow switchMahdi Abbasi0Shima Maleki1Gwanggil Jeon2Mohammad R. Khosravi3Hatam Abdoli4Department of Computer Engineering Faculty of Engineering Bu‐Ali Sina University Hamedan IranDepartment of Computer Engineering Faculty of Engineering Bu‐Ali Sina University Hamedan IranDepartment of Embedded Systems Engineering College of Information Technology Incheon National University Yeonsu‐gu Incheon KoreaComputer Engineering Department Persian Gulf University Bushehr IranDepartment of Computer Engineering Faculty of Engineering Bu‐Ali Sina University Hamedan IranAbstract Software‐defined networks have been developed to allow the entire network to be managed as a programmable entity. As a well‐known protocol in this field, OpenFlow installs new packet forwarding rules of the distinct packets of Big Data flows (known as flow entries) in the flow tables of network switches in order to implement the desired management policies. Despite the high speed, flow tables have limited capacity to store the information of Big Data flows. As a result of inefficient policy for replacing the entries of the flow table, lack of flow entries corresponding to the incoming packets in the flow table of the switch will increase the references to the controller for forwarding this packet as well as the amount of delay in packet forwarding. The underlying idea of the proposed method is to make use of the popularity of traffic flows in the table to select the intended flow for the replacement. For replacement of flow table entries, a novel and intelligent method is proposed in this research which uses a reference history of flows to assign an importance degree to each table entry. Comparison of the simulation results confirms the superiority of the method for reducing the controller's overflow.https://doi.org/10.1049/cmu2.12328
spellingShingle Mahdi Abbasi
Shima Maleki
Gwanggil Jeon
Mohammad R. Khosravi
Hatam Abdoli
An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
IET Communications
title An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
title_full An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
title_fullStr An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
title_full_unstemmed An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
title_short An intelligent method for reducing the overhead of analysing big data flows in Openflow switch
title_sort intelligent method for reducing the overhead of analysing big data flows in openflow switch
url https://doi.org/10.1049/cmu2.12328
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