Service Function Chaining and Traffic Steering in SDN using Graph Neural Network

Rafiq, Adeel; Khan, Talha Ahmed; Afaq, Muhammad; Song, Wang‐Cheol

doi:10.1109/ictc49870.2020.9289378

Cited by 12 publications

(16 citation statements)

References 24 publications

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“…Te graph neural network of this model trains 2 functions: the transfer function of the point and the output function. Te transition function of node n inputs the features of n, all adjacent edge features, all adjacent node features and states, and outputs the state of node n. Te output function computes nodes based on the state and characteristics of the point's output [39]. Li et al [40] used GNN to predict NFV resource requirements so as to obtain advanced information about upcoming requests and improve the efectiveness of SFC reconstruction algorithms based on deep reinforcement learning.…”

Section: Gcn In Communicationmentioning

confidence: 99%

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti

Tang

et al. 2023

International Journal of Intelligent Systems

130

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Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

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Section: Gcn In Communicationmentioning

confidence: 99%

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti

Tang

et al. 2023

International Journal of Intelligent Systems

130

View full text Add to dashboard Cite

show abstract

“…which extends the application of neural networks from Euclidean structure data to non-Euclidean structure data. GNN is based on the message passing mecha- IFIP Networking Conference (IFIP Networking) [63] International Conference on Information Networking (ICOIN) [64,65] International Conference on Information and Communication Technology Convergence (ICTC) [66] International Conference on Network and Service Management (CNSM) [67,68,69] International Conference on Real-Time Networks and Systems (RTNS) [70] International Conference on Wireless Communications and Signal Processing (WCSP) [71] International Conference on emerging Networking EXperiments and Technologies (CoNEXT) [72] International Symposium on Networks, Computers and Communications (IS-NCC) [73] Opto-Electronics and Communications Conference (OECC) [74] Table 4: List of source workshops and the corresponding studies we cover in this study.…”

Section: Journal Name Studiesmentioning

confidence: 99%

“…Last but not the least, Service Function Chaining (SFC) is considered in several studies [112,45,66,57]. SFC uses SDN's programmability to create a service chain of connected virtual network services, resulting in a service function path that provides an end-to-end chain and traffic steering through them.…”

Section: Gnn-basedmentioning

confidence: 99%

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Graph-based Deep Learning for Communication Networks: A Survey

Jiang

2021

Preprint

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Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different communication networks, e.g. wireless networks, wired networks, and software-defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.

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“…For example, in anomaly detection, GNN can be used to learn the expected behavior of a system and then identify anomalies or outliers based on deviations from that behavior 25 . In an SDN‐based solution, GNN can analyze network traffic flow and detect anomalies such as network intrusion, DDoS attacks, or abnormal traffic patterns 26 . SDN is an architecture that allows network administrators to programmatically control and manage network behavior programmatically, making deploying and managing complex networks easier.…”

Section: Introductionmentioning

confidence: 99%

Unleashing the power of SDN and GNN for network anomaly detection: State‐of‐the‐art, challenges, and future directions

Dhadhania,

Bhatia,

Mehta

et al. 2023

Security and Privacy

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Modern computer networks' increasing complexity and scale need serious attention towards network anomaly detection. Software‐defined networking (SDN) and graph neural networks (GNN) have emerged as promising approaches for anomaly detection due to their ability to capture dynamic network behavior and learn complex patterns from large‐scale network data. The amalgamation of SDN and GNN for network anomaly detection presents promising opportunities for improving the accuracy and efficiency of network anomaly detection. This paper focuses on various trends, issues, and challenges by integrating GNN on the top of SDN for network anomaly detection. The article highlights the advantages of using SDN for providing fine‐grained control and programmability in network monitoring. At the same time, GNN can model network behavior as a graph and learn representations from graph‐structured data. The authors also discuss the limitations of traditional anomaly detection methods in SDN, such as rule‐based approaches, and the potential of GNN to overcome these limitations by leveraging their ability to capture non‐linear and dynamic patterns in network data. This paper also presents a case study of DoS attack detection using SDN. The result shows that SDN based approach helps to detect attacks with an accuracy of 97% with future research directions.

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Service Function Chaining and Traffic Steering in SDN using Graph Neural Network

Cited by 12 publications

References 24 publications

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Graph-based Deep Learning for Communication Networks: A Survey

Unleashing the power of SDN and GNN for network anomaly detection: State‐of‐the‐art, challenges, and future directions

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