High Level Policies in SDN

Polčák, Libor; Caldarola, Leo; Choukir, Amine; Cuda, D.; Dondero, Marco; Ficara, Domenico; Franková, Barbora; Holkovič, Martin; Muccifora, Roberto; Trifilo, Antonio

doi:10.1007/978-3-319-30222-5_2

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These policies, which are applicable to the control plane, can cover a wide range of applications such as routing, security, access control, quality of service, etc. [386]. The control plane can enforce/translate policies by programming network devices to perform specific actions once certain conditions are met using a policy engine.…”

Section: Policy Enforcementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

View full text Add to dashboard Cite

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.

show abstract

Section: Policy Enforcementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

View full text Add to dashboard Cite

show abstract

“…As 5G ambition is to unify many services through one platform thus providing basis for further development of systems and applications, it is of high importance to have a valid DPI that will provide viable results and in the same time that will not increase the network latency. There are researches that are focused on DPI in SDN [11], [12], [13], while others are focused on the security aspects of DPI [14], [15], often proposing an introduction of probes or SDN appliances within the network. Network traffic classification in traditional networks is researched in the works of [16] and [17], but they do not consider the use of ML algorithms in NFV environment.…”

Section: Related Workmentioning

confidence: 99%

Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Ilievski¹,

Latkoski²

2020

RADIOENGINEERING

View full text Add to dashboard Cite

Cloudification of all computing environments is an undergoing process. The process has overpassed the classical Virtual Machines (VM) and Software-Defined Networking (SDN) approach and has moved towards dockizing, microservices, app functions, network functions etc. 5G penetration is another trend, and it is built on such platforms. In this environment we are investigating the efficiency of supervised machine learning algorithms for classification of regular and encrypted Voice over IP (VoIP) traffic that 5G relies on, within a virtualized Network Functions Virtualization (NFV) environment and an east-west based network traffic. We are using statistical methods for classification of network packets without the need of inspecting the payload data and without the source, destination and port information of the packets. The efficiency is analyzed from a point of precision of the classification, but also from a point of time consumption, as adding delay to the original traffic may cause a problem, especially within 5G environments where packet delay is crucial.

show abstract

Network Traffic Classification Using Supervised Machine Learning Algorithms in Systems with NFV Architecture

Ilievski

Latkoski

2022

Complex Systems: Spanning Control and Computational Cybernetics: Applications

View full text Add to dashboard Cite

High Level Policies in SDN

Cited by 6 publications

References 10 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Network Traffic Classification Using Supervised Machine Learning Algorithms in Systems with NFV Architecture

Contact Info

Product

Resources

About