Performance Assessment for different SDN-Based Controllers

Sheikh, Mohammad Nowsin Amin; Hwang, I‐Shyan; Ganesan, Elaiyasuriyan; Kharga, Razat

doi:10.1109/wocc53213.2021.9603050

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Using Mininet Simulator, analysis and assessment were performed on several aspects such as throughput, latency, packet loss, jitter, bandwidth usage, etc. We used Mininet to investigate a number of SDN controllers in our preliminary research [69,70,78,88]. We took into consideration a custom topology network situation.…”

Section: Mininet and Mininet Based Studymentioning

confidence: 99%

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

Sheikh,

Hwang,

Raza

et al. 2024

Computers

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An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

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Section: Mininet and Mininet Based Studymentioning

confidence: 99%

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

Sheikh,

Hwang,

Raza

et al. 2024

Computers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using Mininet Simulator, analysis and assessment were performed on several aspects such as throughput, latency, packet loss, jitter, bandwidth usage, etc. We used Mininet to investigate a number of SDN controllers in our preliminary research [68,69,77,88]. We took into consideration a custom topology network situation.…”

Section: Mininet and Mininet Based Studymentioning

confidence: 99%

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers by Mininet Emulator

Sheikh,

Hwang,

Raza

et al. 2024

Preprint

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Software-defined networking (SDN) divides a traditional network into a programmable data plane and a centralized control plane. This technology is expected to transform traditional network companies that provide flexible and intelligent network operations. The core component utilized to oversee every data plane action is the controller in the control plane. In order to achieve optimal performances, the controller’s performance and capabilities are therefore crucial. There are lots of controller research available in existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists for them. This article also includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool called Mininet-based SDN controller performance assessment in this study. Using six performance metrics—bandwidth, round- trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers against two custom network scenarios (uniform and non-uniform host distribution). Our investigation shows that the OpenDayLight controller performs the best across all performance criteria, with the exception of delay, packet loss and round trip time, where the Ryu controller excels. In the entire experiment, the HyperFlow and ONOS controller performs the worst in terms of all performance metrics. Finally, we discuss detailed research findings on the performance. These experimental results provide a decision-making guideline when selecting a controller.

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“…SDN switches typically handle the data plane and execute controller commands. This change in control architecture improves network management flexibility and manageability [3,4]. SDN provides answers to a variety of network difficulties.…”

Section: Introductionmentioning

confidence: 99%

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Raza,

Sheikh,

Hwang

et al. 2024

Telecom

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SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions.

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Performance Assessment for different SDN-Based Controllers

Cited by 3 publications

References 3 publications

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers by Mininet Emulator

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

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