An Ontology-Based Information Extraction System for bridging the configuration gap in hybrid SDN environments

Martinez, A.; Yannuzzi, M.; Vergara, Jorge E. López de; Serral-Gracià, R.; Ramírez, Wilson

doi:10.1109/inm.2015.7140321

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…OF-CONFIG allows automatic discovery of switches, provides a standard way to configure the switches by installing flow tables and other settings, monitors the status of switches using traffic flow statistics, etc., and updates the switches. However, some have complemented the capabilities of OF-CONFIG/NETCONF while presenting a semantic-based approach that automates the configuration of network devices, where they have formalized the semantics of the switch configuration domain using a Web Ontology Language (OWL) and developed an ontology-based information extraction system for the Command Line Interface (CLI) of network devices [252]. They have further developed a learning algorithm to enable automated interpretation of the command-line interface's configuration capabilities in the network.…”

Section: Network Management Protocols/interfacesmentioning

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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Section: Network Management Protocols/interfacesmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…It is also a convenient solution to overcome interoperability issues. In [18], the authors take advantage of Natural Language Processing (NLP) frameworks to implement a solution to extract information from particular models and to integrate these pieces of information in a more open and interoperable description of the domain, i.e., an ontology.…”

Section: Related Workmentioning

confidence: 99%

Towards a holistic semantic support for context-aware network monitoring

et al. 2020

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Monitoring current communication networks and services is an increasingly complex task as a result of a growth in the number and variety of components involved. Moreover, different perspectives on network monitoring and optimisation policies must be considered to meet context-dependent monitoring requirements. To face these demanding expectations, this article proposes a semantic-based approach to support the flexible configuration of context-aware network monitoring, where traffic sampling is used to improve efficiency. Thus, a semantic layer is proposed to provide with a standard and interoperable description of the elements, requirements and relevant features in the monitoring domain. On top of this description, semantic rules are applied to make decisions regarding monitoring and auditing policies in a proactive and context-aware manner. Use cases focusing on traffic accounting and traffic classification as monitoring tasks are also provided, demonstrating the expressiveness of the ontology and the contribution of smart SWRL rules for recommending optimised configuration profiles.

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“…Management of complex 5G networks using ontologies is proposed in [17]. Martinez et al [18] propose an ontology-based information extraction system for bridging the configuration gap in hybrid Software-Defined Networks (SDN). The focus is on formalization of switch and router configuration domain to ease the configuration overhead for administrators.…”

Section: Ontology-based Network Managementmentioning

confidence: 99%

An ontology-based Plug-and-Play approach for in-vehicle Time-Sensitive Networking (TSN)

Farzaneh

Knoll

2016

2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

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Abstract-Time-Sensitive Networking (TSN) standards aim to support hard real-time communication with minimum latency based on Ethernet technology. They can also support the automotive domain considering upcoming and challenging application requirements in intelligent vehicles of the future. Despite all advantages, Time-Aware Shaper (TAS), a significant feature of TSN, increases the network configuration overhead. This configuration is required to guarantee deterministic network behavior. In this paper, an ontology-based approach is presented that extends TSN capabilities regarding Plug-and-Play and automatic network configuration. The developed ontology meta-models can be used by automotive experts (e.g. network designers or engineers) to design a concrete in-vehicle network based on TSN including knowledge that is required for automatic configuration.

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An Ontology-Based Information Extraction System for bridging the configuration gap in hybrid SDN environments

Cited by 9 publications

References 12 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Towards a holistic semantic support for context-aware network monitoring

An ontology-based Plug-and-Play approach for in-vehicle Time-Sensitive Networking (TSN)

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