Analysing the performance of the OpenFlow standard for software-defined networking using the OMNeT&amp;#x002B;&amp;#x002B; network simulator

Banjar, Ameen; Pupatwibul, Pakawat; Braun, Robin; Moulton, Bruce

doi:10.1109/apcase.2014.6924467

Cited by 9 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, SDN differs from traditional networks by virtualizing the network components, giving the administrators a new level of visibility, which brings significant benefits like efficiency and agility to the network. Compared to The Open Systems Interconnection model (OSI model) (Figure 3) (Banjar et al, 2014). The OSI is a model that describes how computer systems communicate over a network using seven layers.…”

Section: Software Defined Networking (Sdn)mentioning

confidence: 99%

See 1 more Smart Citation

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

Bouchmal,

Cimoli,

Stabile

et al. 2023

Front. Comms. Net.

View full text Add to dashboard Cite

The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies.

show abstract

Section: Software Defined Networking (Sdn)mentioning

confidence: 99%

mentioning

confidence: 99%

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

Bouchmal,

Cimoli,

Stabile

et al. 2023

Front. Comms. Net.

View full text Add to dashboard Cite

show abstract

“…Research conducted to compare the performance of SDN with conventional networks based on the parameters of jitter, delay, and throughput showed that SDN had better delay, jitter, and throughput results (Hernandez, Jimenez, Pranolo, & Rios, 2019). In another study, it was found that, by analyzing key network metrics including round-trip-time (RTT) and data transfer rate (DTR), the results show that the location of the controller has a demonstrable effect on network performance (Banjar, Pupatwibul, Braun, & Moulton, 2014). The SDN network has Quality of Services (QoS) results compared to conventional networks and the latency value produced by SDN is between 0.019 -0.084ms (Nugroho, Irfan, & Faruq, 2019).…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of software defined network performance and conventional based on latency parameters

Harahap

Irmayani

Muti’ah

2022

SinkrOn

View full text Add to dashboard Cite

Software Defined Network (SDN) offers convenience in managing network devices by simply setting the software control plane so as to reduce the complexity of network configuration. However, the ease of management offered is not always accompanied by an increase in network performance when compared to conventional network architectures. This study will perform a performance comparison analysis between networks with SDN architecture (OpenFlow) and conventional architecture. The research methods applied are: topology implementation on Mininet, network simulation and data collection, data analysis, and comparative analysis. Network performance testing is carried out based on latency parameters that are simulated in one subnet using the Mininet emulator. From the results of the latency test, it is found that the average latency on the SDN network is 0.119ms, while the average latency on the conventional network is 0.09588ms. Based on these results, it can be concluded that the network topology that has better performance based on latency parameters is a conventional network topology with an average latency value of 0.09588ms.

show abstract

Enabling Heterogeneous 5G Simulations with SDN Adapters

Pham

McMahon

Nguyen

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Analysing the performance of the OpenFlow standard for software-defined networking using the OMNeT++ network simulator

Cited by 9 publications

References 9 publications

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

Comparative analysis of software defined network performance and conventional based on latency parameters

Enabling Heterogeneous 5G Simulations with SDN Adapters

Contact Info

Product

Resources

About