Survey on application programming interfaces in software defined networks and network function virtualization

Krishna, S Murali; Sharma, Rinki

doi:10.1016/j.gltp.2021.08.018

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…8.1.4 SDN and DL interfacing problem. API offers a simplified way of system integration and fast prototyping (Krishna and Sharma, 2021). SDN was meant for programmable networks, which seek to make more robust device architectures in terms of traffic management and forwarding.…”

Section: Conducting the Reviewmentioning

confidence: 99%

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Chanhemo

Mohsini

Mjahidi

et al. 2023

IJICC

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PurposeThis study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.Design/methodology/approachThe study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.FindingsFollowing the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.Originality/valueThis study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

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Section: Conducting the Reviewmentioning

confidence: 99%

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Chanhemo

Mohsini

Mjahidi

et al. 2023

IJICC

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“…This technique allows for a comprehensive analysis that provides insight into the intensity, seasonality, and type of attack. Hari Krishna & Sharma (2021) [21] presented an overview of applications' programming interfaces in SDN and the virtualization of network functions is given. The authors propose a list of effective and efficient orchestration approaches differing in design for microservices in SDN and NFV domains Yang et al (2021) [22] highlight flow routing as one of the most critical problems in SDN.…”

Section: -1-literature Review and Analysismentioning

confidence: 99%

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

et al. 2022

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The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PDF

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“…By dynamically automating SDN applications, SDN helps network administrators to simply deploy, administer, safeguard, and maximize network capabilities that can be programmed due to the fact that applications do not rely on software in the custody of other enterprises or companies. Since network control is independent of sending ability, the network control can be directly programmed [29], as shown in Figure 4.…”

Section: Properties Of Sdnmentioning

confidence: 99%

Software-Defined Networks for Optical Networks Using Flexible Orchestration: Advances, Challenges, and Opportunities

Nsafoa-Yeboah

Tchao

Yeboah-Akowuah

et al. 2022

Journal of Computer Networks and Communications

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Optical networks offer a wide range of benefits to the telecommunication sector worldwide with their provision of higher bandwidth, which leads to faster data speed, longer transmission distance, and improved latency. Currently, the complexity associated with advancements in optical networks poses problems to network flexibility, reliability, and quality of service. Over the years, many reviews and proposals have been implemented by several literature studies to provide solutions for optical networks using software-defined networks and network service orchestrators. This study reviews the significant challenges in current optical network applications, the various solutions rendered by software-defined networks, and network service orchestration, impediments, and gaps in these software-defined networks. This study will go a step further to look into the various improvements and implementations of software-defined networks tailored to solve specific optical network problems. This study further proposes a flexible orchestration architecture for software-defined networks for solving flexibility and scalability problems in optical networks. This proposal uses an open network system (ONOS) SDN controller, leveraging on dockerisation and Kubernetes clusterisation and orchestration. This solution presents a more flexible, reliable, customable, and higher quality of service, which is an improvement upon current solutions in the literature.

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Survey on application programming interfaces in software defined networks and network function virtualization

Cited by 7 publications

References 20 publications

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

Software-Defined Networks for Optical Networks Using Flexible Orchestration: Advances, Challenges, and Opportunities

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