Software Defined Networks: a Comparative Study and Quality of Services Evaluation

Nugroho, Herry Prasetyo; Irfan, Muhammad; Faruq, Amrul

doi:10.15294/sji.v6i2.20585

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Traditional computer networking (N0) recorded the highest bandwidth, while N1 recorded the highest throughput and significant impact on the jitter column. The result corroborated [16] as they observed significant F-value and stability in the SDN application-awareness experiment.…”

Section: Resultssupporting

confidence: 81%

“…The researchers determined the aspects that enable Software-Defined Networking to provide quality of service features in data networks using network simulation over the same base network and under the same working conditions by carrying out measurements of the packet forwarding response time and management of the transported bandwidth. Nugroho et al [16] reported that the QoS analysis of the Software-Defined Network architecture performed better than conventional traditional network architectures with the value of latency delay on the Software-Defined Network ranging from 0.019ms to 0.084ms, and with 0% packet loss when it addressed network traffics of 10-100Mbps. The authors reported the Quality of Service (QoS) performances between the two networks employing SDN-based architecture and without SDN-based architecture using Mininet as a software emulator.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Traditional and Software-Defined Networking on Performance of Computer Network

Sadiku

Ajayi

Sakpere

et al. 2022

SJI

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Purpose: Computer networks and the Internet are changing the way we communicate, learn, work, and even play. Conventional computer networks are not smart enough towards processes that contribute to improving online control transaction of services and demand for unlimited communication services. Hence, computer networking has to go smart.Methods: This paper explores the effect of different computer networking types - traditional computer networking (D0) and Software-Defined Networking (D1). The paper combined traditional computer networking (D0) with Software-Defined Network (D2) running applications (A1, A2, A3, A4 and A5) with the host sending 5 packets (P1, P2, P3, P4 and P5) across the networks emulated using Mininet network emulation to observe various performance parameters on the network.Result: It was observed that Application A1 recorded the highest bandwidth, throughput and latency. The least bandwidth, throughput and latency were observed in A4. The result showed that below 80% of the IPv4 packet size (65,507 bytes) of running application, the higher the bandwidth the higher the throughput. Also, the lower the latency the more statistically similar the jitter experienced. Packet P1 has the highest bandwidth and throughput usage with high latency. The results indicate that the higher the bandwidth and throughput, the higher the latency observed in the packet sent across the network. Traditional computer networking (D1) recorded the highest bandwidth and throughput with the highest jitter. The correlation result showed that the jitter decreases with increasing bandwidth and throughput.Novelty: This study provides information on traditional computer networking and Software-Defined Networking. The result validates studies that observed significant F-value and stability in the SDN application-awareness experiment.

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Section: Resultssupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Effect of Traditional and Software-Defined Networking on Performance of Computer Network

Sadiku

Ajayi

Sakpere

et al. 2022

SJI

View full text Add to dashboard Cite

show abstract

“…To facilitate this assessment, the study employs simulation techniques, specifically using the SDN network emulator and the MiniNet Floodlight controller. A range of Quality of Service (QoS) parameters, including latency delay, jitter, throughput, and packet loss, is employed to gauge the QoS metrics of the latter network [19].…”

Section: A Data Collectionmentioning

confidence: 99%

Enhancing Quality-of-Service in Software-Defined Networks Through the Integration of Firefly-Fruit Fly Optimization and Deep Reinforcement Learning

Aboughaly,

Hannan

2024

IJACSA

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The Software Defined Networking (SDN) paradigm has emerged as a critical tool for meeting the dynamic demands of network management with respect to efficiency and flexibility. Quality of Service (QoS) optimization, which encompasses essential features including bandwidth allocation, latency, and packet loss, is a major problem in SDN systems due to its direct influence on network application performance and user experience. To deal with these important issues, this paper tackles the critical problem of Software-Defined Networks (SDNs) Quality-of-Service (QoS) optimization, which is a critical factor affecting network application performance and user experience. Within the Firefly-Fruit Fly Optimised Deep Reinforcement Learning (DQ-FFO-DRL) framework, a novel combination of optimization techniques derived from Fruit Fly and Firefly behaviors with Deep Q-Learning is presented in this suggested approach, which is called Deep Q-Learning. The framework effectively investigates ideal network configurations by utilizing the distinct advantages of the Fruit Fly and Firefly optimization components, while the Deep Q-Learning component dynamically adjusts to changing network circumstances by drawing conclusions from prior experiences. Extensive testing and modeling reveal that the DQ-FFO-DRL approach performs very well in SDNs compared to conventional QoS management solutions. When it comes to negotiating the always changing world of resource allocation, network usage, and overall network performance, this algorithm demonstrates exceptional adaptability. The suggested system, which is implemented in Python, offers an advanced and flexible method for enhancing QoS in SDN systems.

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“…Penelitian kelima dilakukan oleh Govindarajan, et al, untuk meneliti QoS Controller pada jaringan SDN Cloud Computing pada studi kasus layanan video streaming [13]. Penelitian keenam dilakukan oleh Nugroho untuk melakukan perbandingan nilai QoS pada jaringan SDN dengan jaringan komputer konvensional [14] Untuk kebutuhan hardware digunakan komputer pribadi dengan spesifikasi: ASUS A456UR Intel(R) Core (TM) i5-6200U CPU @ 2.30GHz (4CPUs), memori DDR4 4096MB, HDD 1TB. Selain itu juga ditambahkan dengan koneksi internet untuk kemudahan penggunaan repositori online.…”

Section: Pendahuluanunclassified

Pengujian QoS Pada Implementasi SDN Berbasis Mininet dan OpenDaylight Menggunakan Topologi Tree

Pratama

Bakkara

2021

SISFOKOM

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The development of information technology and computer network from time to time is increasing along with the increase in user needs for both from the business, education, industrial, to data security side. Data of network traffic that is getting denser in communication and data exchange between users on computer networks can become a problem when using conventional computer network technology. For that, it needs a new technology that is implemented in computer networks, along with the measurement of Quality of Service (QoS) in it. Software-Defined Networking (SDN) is a solution for this, where the stages of network design, management and implementation, separate the data plane and the control plane. In this research, the implementation of SDN was carried out in the form of a simulation using both of Mininet and OpenDaylight with a Tree Topology, then the QoS measurements were carried out in it. The results of testing and measuring QoS on SDN simulations with Tree topology using Mininet and OpenDaylight, showed a Jitter value of 0.425 ms, a Packet Loss value of 0.266%, a Bandwith value of 9.3925 Mbps, a UDP Throughput value of 2.348 bits/sec, and a TCPThroughput value of 2.335 bits/sec.

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Software Defined Networks: a Comparative Study and Quality of Services Evaluation

Cited by 4 publications

References 14 publications

Effect of Traditional and Software-Defined Networking on Performance of Computer Network

Effect of Traditional and Software-Defined Networking on Performance of Computer Network

Enhancing Quality-of-Service in Software-Defined Networks Through the Integration of Firefly-Fruit Fly Optimization and Deep Reinforcement Learning

Pengujian QoS Pada Implementasi SDN Berbasis Mininet dan OpenDaylight Menggunakan Topologi Tree

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