Management of Software-Defined Networking Powered by Artificial Intelligence

Ali, Jehad; Roh, Byeong‐hee

doi:10.5772/intechopen.97197

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…At present, there is a large development in SDN and AI technologies, which made it feasible to design and control IoT networks [11]. In this paper, the authors describe how AI and SDN technologies can be integrated to improve the security and functionality of these IoT networks [12]. The IoT devices exchange data over the Internet and with recent advances in network technologies such as cloud and edge computing, the security issues must be addressed highlighted by authors in a research paper.…”

Section: Literature Reviewmentioning

confidence: 99%

The Comparison of Routing Algorithm for SDN Network using AI for Future Network

Ahmed,

Siddiqui,

Rizvi

et al. 2024

VAWKUM trans. comput. sci.

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After the invention of new IoT devices the new connection has been added in the network and bulk of data flow in the system. AI is very famous algorithum and software-defined networking (SDN) is new fast network controlling method both can be integrate for fast and secure network-related scenarios and finding great experience and growing interest in the research community. The present network connected to many number of objects over the Internet and present a complex scenario. To ensure security, privacy, confidentiality, and programmability, the architecture of AI and blockchain for SDN network is proposed in this paper. For network service provider this is ample time to apply artificial intelligence (AI) and blockchain (BC) in a software-defined network controller to ensure a secure network control, traffic management and system optimimum performnce. Also for shortest path to reduce delay in the network, the best routing algorithum must be apply in the network. The AI (artificial intelligence) algorithum can help SDN controller to fastdecision to reduced delay and can provide security in the network. In this paper routing algorithm also will be investigate faster Network.

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Section: Literature Reviewmentioning

confidence: 99%

The Comparison of Routing Algorithm for SDN Network using AI for Future Network

Ahmed,

Siddiqui,

Rizvi

et al. 2024

VAWKUM trans. comput. sci.

View full text Add to dashboard Cite

show abstract

“…In this paper, the authors highlighted that the artificial intelligence technology with an SDN controller is more suitable, more accurate, and effective for traffic scheduling. If it is based on traffic types, it will improve the use of network resource optimization problems for operators [3]. The authors highlighted that the traditional network architecture has a single static processing method, which is unsuitable for data center network traffic scheduling and suffers from many issues.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For routers, it was not possible to keep all information about the shortest path and traffic updates in the network. [3]. To solve this issue the researchers proposed an SDN controller by separating the control of the network from the router and creating separate nodes to monitor and control the traffic in the network.…”

Section: Introductionmentioning

confidence: 99%

The Analysis the Performance of SDN Controller and AI System for Future Network

Ahmed,

Abdul moid,

Ismat

et al. 2024

VAWKUM trans. comput. sci.

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the SDN is new network controller concept and it provide faster packet data receiving and dynamic path in large network. It can work with old systems via creating new nodes. It can monitor activities of traffic routing, flowing and congestion and on the basis of these updated from all routers in the network. It can provide best shortest route to data packet in few seconds. But the main issue is that its location and controlling of huge data. The network controller can be track and data can be hack by hackers. The AI (artificial intelligence) system can help SDN controller to work batter, improve speed of the system and provide security to control network. In this paper, the methods working of AI with SDN controller will be analyzed to make system faster and save from cyber-attack and a solution will be proposed based on analyses of SDN Network.

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“…A resource allocation model (RAM) is hinged on a Software distribution Network (SDN)-managed Mobile Edge computing architecture. The work in [51] discussed several techniques involving AI to manage SDN networks and proposed a SDN management system powered by AI termed as SDNMS-PAI for handling end-to-end networks. The SDN has the function of establishing control of the server resource allocation and allocating resource necessary for data processing requirements separately.…”

Section: Multimedia Streaming On Intelligence Edgementioning

confidence: 99%

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

et al. 2022

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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

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Management of Software-Defined Networking Powered by Artificial Intelligence

Cited by 8 publications

References 37 publications

The Comparison of Routing Algorithm for SDN Network using AI for Future Network

The Comparison of Routing Algorithm for SDN Network using AI for Future Network

The Analysis the Performance of SDN Controller and AI System for Future Network

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

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