Performance Analysis of POX and Ryu with Different SDN Topologies

Ali, Jehad; Lee, Seungwoon; Roh, Byeong‐hee

doi:10.1145/3209914.3209931

Cited by 34 publications

(16 citation statements)

References 11 publications

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“…The performance of the controller shall be validated in the real-world Internet and Brite topologies. In our previous work [24], we compared the performance of the POX and RYU controllers in various topologies. These topologies included Single, Linear, Tree, Dumbbell, and data center networks (DCNs).…”

Section: Introductionmentioning

confidence: 99%

QoS improvement with an optimum controller selection for software-defined networks

2019

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The software-defined networking (SDN) paradigm has simplified the management of computer networks by decoupling data and control planes. Moreover, the separation of the data and control planes has transitioned network complexity from traditional devices to controllers; therefore, controllers have become indispensable entities in SDN. Controllers have multiple features and direct the network from a central point and respond to updates to topological changes. However, the supportive capability of these features is strong in one controller but weak in another. Due to several controllers and each controller having a set of features, selecting an optimal SDN controller can be considered to be a multi-criteria decision-making (MCDM) problem. Herein, a two-step approach is proposed for SDN controller selection. First, the controllers are ranked with analytical network process (ANP) according to their qualitative features which influence the performance of these controllers and then a performance comparison is performed to check for the QoS improvement. The controller with a high-weight value from the feature-based comparison is quantitatively analysed by experimental analysis. The main contribution of this paper is checking the applicability of the ANP for controller selection in SDN considering its features and performance analysis in real-world Internet and Brite topologies. The simulation results show that the controller computed through the proposed approach outperforms the controller selected with existing approaches. The selection of an optimum controller with ANP results in a reduction of topology discovery time and delay in the normal and traffic load scenario. Similarly, an increase in throughput with a reasonable utilization of the central processing unit (CPU) is observed for the proposed controller.

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Section: Introductionmentioning

confidence: 99%

QoS improvement with an optimum controller selection for software-defined networks

2019

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“…Penelitian Jehad Ali, Seungwoon Lee, Byeong-hee Roh [12] membandingkan performa dari dua controller jaringan SDN antara Ryu dan Pox Controller. Pengujian controller dilakukan berdasarkan skenario topologi yang berbeda: Single, Linear, Tree.…”

Section: A Studi Literaturunclassified

Performansi Software Defined Network Controller Pada Streaming Video Menggunakan Real-time Transport Protocol

Kodar

Gunawan²,

Rahmatulloh³

2021

JuTISI

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Computer network conditions that are dynamic and also complex make network operators often make mistakes in the configuration of a network device. However, these mistakes can be resolved by the emergence of a centralized programming approach which is now known as Software Defined Network (SDN). In addition, with the development of information technology, several services in computer networks also grabbed a lot of attention from the world, such as video streaming services. A video streaming service that is so massive is capable of having an impact on data traffic on computer networks. In SDN networks, controllers are a core component of this architecture. Currently, many different types of SDN controllers can be used, however, choosing a controller in an SDN network must be an important concern. This is related to the performance of the SDN controller which is a requirement for SDN network development. This research will analyze the performance of the video streaming service using the Real-time Transport Protocol (RTP) streaming protocol in testing two SDN controllers, namely Pox and Opendaylight. Based on the tests that have been carried out, it produces Quality of Service (QoS) values in the form of throughput, delay, and jitter. The Pox controller excels in the value of QoS delay and throughput. As for the jitter value, both Pox and Opendaylight controllers have good indexes.

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“…The lower latency requirement leads to our preference for Ryu SDN controller since Ryu exhibits the best latency results in comparison with ONOS, OpenDayLight and Floodlight [41]. In addition, Ryu has better TCP throughput as compared to POX [42]. Moreover, Ryu is python-based which is much easier to integrate with existing mainstream machine learning libraries (e.g., scikit-learn in this paper).…”

Section: Online Detectionmentioning

confidence: 99%

Machine Learning based Malicious Payload Identification in Software-Defined Networking

Cheng¹,

Wu²,

Zhou³

et al. 2021

Preprint

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Deep packet inspection (DPI) has been extensively investigated in software-defined networking (SDN) as complicated attacks may intractably inject malicious payloads in the packets. Existing proprietary pattern-based or port-based third-party DPI tools can suffer from limitations in efficiently processing a large volume of data traffic. In this paper, a novel OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on the SDN paradigm to provide adaptive and efficient packet inspection. First, OFDPI prescribes an early detection at the flow-level granularity by checking the IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows for deep packet inspection at the packet-level granularity: (i) for unencrypted packets, OFDPI extracts the features of accessible payloads, including tri-gram frequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and linguistic features. These features are concatenated into a sparse matrix representation and are then applied to train a binary classifier with logistic regression rather than matching with specific pattern combinations. In order to balance the detection accuracy and performance bottleneck of the SDN controller, OFDPI introduces an adaptive packet sampling window based on the linear prediction; and (ii) for encrypted packets, OFDPI extracts notable features of packets and then trains a binary classifier with a decision tree, instead of decrypting the encrypted traffic to weaken user privacy. A prototype of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The performance and the overhead of the proposed sulotion are assessed using the real-world datasets through experiments. The numerical results indicate that OFDPI can provide a significant improvement in detection accuracy with acceptable overheads.

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Performance Analysis of POX and Ryu with Different SDN Topologies

Cited by 34 publications

References 11 publications

QoS improvement with an optimum controller selection for software-defined networks

QoS improvement with an optimum controller selection for software-defined networks

Performansi Software Defined Network Controller Pada Streaming Video Menggunakan Real-time Transport Protocol

Machine Learning based Malicious Payload Identification in Software-Defined Networking

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