On Designing SDN Services for Energy-Aware Traffic Engineering

Assunção, Marcos Dias de; Carpa, Radu; Lefèvre, Laurent; Glück, Olivier

doi:10.1007/978-3-319-49580-4_2

Cited by 3 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Energy-aware routing has been employed with energy-aware application services such as tunneling for fast rerouting, smooth node disabling, and detection of traffic spikes and link failures, which has reduced the energy consumption of internet service providers by 5% to 35% [474]. In [475], the authors discuss the feasibility of an application that implements services in an ONOS network operating system to implement energy-aware traffic engineering strategies. Therefore, routing and traffic engineering can also be adapted to optimize the energy efficiency of the network.…”

Section: Network Managementmentioning

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: Network Managementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

View full text Add to dashboard Cite

show abstract

A new approach for energy efficiency in software defined network

Priyadarsini

Bera

Rahman

2018

2018 Fifth International Conference on Software Defined Systems (SDS)

View full text Add to dashboard Cite

A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing

Son

Buyya

2018

ACM Comput. Surv.

View full text Add to dashboard Cite

Software-Defined Networking (SDN) opened up new opportunities in networking with its concept of the segregated control plane from the data-forwarding hardware, which enables the network to be programmable, adjustable, and reconfigurable dynamically. These characteristics can bring numerous benefits to cloud computing, where dynamic changes and reconfiguration are necessary with its on-demand usage pattern. Although researchers have studied utilizing SDN in cloud computing, gaps still exist and need to be explored further. In this article, we propose a taxonomy to depict different aspects of SDN-enabled cloud computing and explain each element in details. The detailed survey of studies utilizing SDN for cloud computing is presented with focus on data center power optimization, traffic engineering, network virtualization, and security. We also present various simulation and empirical evaluation methods that have been developed for SDN-enabled clouds. Finally, we analyze the gap in current research and propose future directions.

show abstract

On Designing SDN Services for Energy-Aware Traffic Engineering

Cited by 3 publications

References 18 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

A new approach for energy efficiency in software defined network

A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing

Contact Info

Product

Resources

About