Design and Implementation of a Benchmarking Tool for OpenFlow Controllers

Gamess, Eric; Tovar, Daniel; Cavadia, Alberto

doi:10.5815/ijitcs.2018.11.01

Cited by 7 publications

(3 citation statements)

References 13 publications

(9 reference statements)

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“…The controller is the most important and fundamental part of the SDN architecture [36]- [38]. In SDN networks, control is carried out centrally in the controller [39], [40].…”

Section: An Overview Of Routing Methods In Transport Networkmentioning

confidence: 99%

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

et al. 2020

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This paper addresses traffic engineering (TE) issues in software-defined vehicular networking (SDVN). A brief analysis of the features of SDVN, which improves the efficiency of TE in SDVN, is presented. The feasibility of using multi-path routing with TE is substantiated. A procedure and an example of the formation of multiple routes based on a modified wave routing algorithm are given. Considering the features of the SDVN technology, a modified TE method is proposed, which reduces both the time complexity of forming multiple paths and the path reconfiguration time. The dynamic path reconfiguration algorithm is presented. INDEX TERMS Intelligent transportation system (ITS), multi-path routing, software-defined network, traffic engineering (TE). I. INTRODUCTION The ever-growing number of vehicles on the roads makes the vehicular ad-hoc network (VANET) an important research topic in the field of automotive and wireless technologies. VANETs are considered as a subclass of classic mobile ad hoc networks (MANETs) [1]-[3]. Where vehicles play the role of mobile nodes of the VANETs. That have embedded advanced equipment on-board, traveling on restricted routes (i.e., roads, streets and lanes), and communicating with each other for information exchange using vehicle-to-vehicle (V2V) communication protocols, and also between vehicles and roadside units (RSU) installed on the side of the roads (i.e., wireless and cellular network infrastructure), to form a link between a Vehicle-to-Infrastructure (V2I). Compared to static or low-speed moving nodes in a traditional wireless network, vehicles move faster and more unpredictably, resulting in frequent changes to VANET network settings [4]-[7]. This places higher demands on routing protocols in VANET networks. Most well-known routing methods are not effective for VANET networks [8]-[11]. In this regard, the urgent The associate editor coordinating the review of this manuscript and approving it for publication was Chao Chen.

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“…The controller is the most important and fundamental part of the SDN architecture [36]- [38]. In SDN networks, control is carried out centrally in the controller [39], [40].…”

Section: An Overview Of Routing Methods In Transport Networkmentioning

confidence: 99%

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

et al. 2020

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“…A multiple-controller architecture can increase network reliability if a switch fails. OpenFlow starts operations when the switch is connected to all its configured controllers simultaneously, whereas related messages are only sent to the next switch [36][37][38][39].…”

Section: Openflowmentioning

confidence: 99%

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

et al. 2021

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In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

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“…The SouthBound API was accustomed to talking with redirection devices like network switches in both directions [3]. OpenFlow protocols manage a controller's routing decision to change the flowhold rules of the flow table in switches [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of QoS in Wired and Wireless SDN Based on Mobile IP

Islam¹,

Rahman²,

Nasir³

2021

IJCNIS

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Software-Defined Networking (SDN) is the new network paradigm whose primary focus is to create a dynamic, scalable and flexible network. It provides programmable functions for implementation of network configuration management. The demand for wireless network functionality is rising simultaneously. Mobility management for a large network is an issue in Internet Engineering Task Force (IETF). There are also a number of solutions to support mobility within the network. The Mobile Internet Protocol (MIP) is used for managing mobility. Furthermore, for the SDN platform, mobility adds roaming capability for mobile nodes in the software-defined wireless network (SDWN). In the wired scenario, SDN has different capabilities to deliver network services according to the fixed node. This study provides a quality of service (QoS) analysis in both SDN and SDWN. Mininet WiFi and RYU network emulator as a controller is used to implement the mobility API. The random walk model is applied as mobility functionality toward the final nodes. Moreover, several QoS measurement matrices are analyzed according to the network topology. At the end, round trip time (RTT), cumulative distributed function (CDF), packet loss and throughput are analyzed for quality of service comparable in the SDN and SDWN scenarios according to the MIP.

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Design and Implementation of a Benchmarking Tool for OpenFlow Controllers

Cited by 7 publications

References 13 publications

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

A Comprehensive Analysis of QoS in Wired and Wireless SDN Based on Mobile IP

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