Flowlet-level multipath routing based on graph neural network in OpenFlow-based SDN

Yan, Binghao; Shen, Jianliang; Liang, Dong

doi:10.1016/j.future.2022.04.006

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…An adaptive flow partitioning scheme has been developed in the paper [19] to achieve multipath transmission under dynamic network state changes, using the rule timeout mechanism that comes with the OpenFlow protocol. The authors proposed using a graph neural network to predict link delay to aid in adaptive flow partitioning and intelligent forwarding path selection.…”

Section: Related Workmentioning

confidence: 99%

Traffic engineering and QoS/QoE supporting techniques for emerging service-oriented software-defined network

Beshley,

Kryvinska,

Beshley

et al. 2024

J. Commun. Netw.

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The future integration of software-defined network (SDN) with the service-oriented architecture (SOA) paradigm requires new solutions to ensure the quality of service (QoS) according to the users' requirements. The paper presents a user experience-centric approach to traffic engineering and QoS/quality of experience (QoE) support for service-oriented software-defined network (SOSDN) architecture. This approach is to enable end-to-end QoS across the networking and computing domain by monitoring and agreeing on the dynamic state of their functioning. The proposed SOSDN is based on improved traffic engineering techniques, such as adaptive prioritization of services, server selection, and QoS/QoE-based routing. The developed adaptive service prioritization algorithm automatically changes the priority of flows in the network operation mode by the SDN controller for individual users under the concluded service level agreements (SLA) contract. We proposed a mathematical model of correlation of user satisfaction level by QoE score with technical QoS parameters. This model is based on the normalized value of the integral additive QoS criterion. Accordingly, ensuring the ordered user-centric QoS/QoE is carried out by means of proposed multi-criteria adaptive routing of data flows, the metric of which is based on the integral additive QoS criterion. The simulation results showed that, in contrast to known practical solutions, the integrated use of the proposed method of adaptive multi-criteria routing and prioritization of data flows provides a high level of QoE required by users in the SOSDN paradigm.Index Terms-Quality of experience (QoE), quality of service (QoS), service level agreements (SLA), service-oriented architecture (SOA), service-oriented software-defined network (SOSDN), software-defined network (SDN) I. INTRODUCTIONT HE quality of service (QoS) management and traffic engineering in communication systems based on a serviceoriented architecture (SOA) paradigm are very important

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Section: Related Workmentioning

confidence: 99%

Traffic engineering and QoS/QoE supporting techniques for emerging service-oriented software-defined network

Beshley,

Kryvinska,

Beshley

et al. 2024

J. Commun. Netw.

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“…A security-oriented routing mechanism called RouteGuardian has been utilized in KDN, which has a network security virtualization framework to detect abnormal traffic and compose that knowledge with the latest network status to generate rules to isolate malicious nodes and reconfigure routes [223]. In another routing framework for KDN, a graph neural network is used for predicting link delay, where the knowledge on predicted delay is composed with adaptive flow splitting using the rule timeout mechanism in the OpenFlow protocol to generate forwarding rules [224]. A QoSaware flow rule aggregation scheme called Q-Flag for knowledge-defined IoT networks first generates knowledge by selecting paths that minimize flow table utilization using a heuristic model-based approach, and then composes the generated knowledge considering both the flow rule capacity of the switches and the QoS requirements of applications to produce aggregated flow rules [225].…”

Section: Examples Using Composed Knowledge In the Existing Literaturementioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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“…Routing Optimization: Data packet prediction can also play a role in optimizing routing algorithms within the network. If a node can predict that certain data packets are likely to be forwarded to a particular sink node, it can optimize its routing decisions accordingly, reducing unnecessary hops and improving network efficiency [20,21]. v. Context-Aware Applications: WSNs are often used in applications where contextawareness is essential, such as environmental monitoring or healthcare.…”

Section: Data Packet Prediction In Low-rate and Low-power Networkmentioning

confidence: 99%

Multi-Network Latency Prediction for IoT and WSNs

Balota,

Kor,

Shobande

2023

Computers

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The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.

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Flowlet-level multipath routing based on graph neural network in OpenFlow-based SDN

Cited by 13 publications

References 24 publications

Traffic engineering and QoS/QoE supporting techniques for emerging service-oriented software-defined network

Traffic engineering and QoS/QoE supporting techniques for emerging service-oriented software-defined network

A Comprehensive Survey on Knowledge-Defined Networking

Multi-Network Latency Prediction for IoT and WSNs

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