Latest Advances in Software Defined Networking (SDN) for Optical Networks

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Optical Communication and Network".

Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 3672

Special Issue Editors


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Guest Editor
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Carl Friedrich Gauss 7, 08860 Castelldefels, Spain
Interests: SDN/NFV; network virtualization; network orchestration; MEC and 5G Networks

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Guest Editor
Telefónica I+D/GlobalCTO, Ronda de la Comunicación, s/n, 28050 Madrid, Spain
Interests: metro and core networks (IP/MPLS, optical transport networks); control plane and management of metro and core networks (SDN, OF, segment routing, GMPLS, ASON, PCE); multilayer and multidomain architectures; networking traffic characterization and dimensioning analysis; access networks (xDSL, HFC, FTTx)

Special Issue Information

Dear Colleagues,

Software-defined networking (SDN) has been in the market for more than ten years with great technological success, allowing network softwarization and becoming a common tool. Since the introduction of the OpenFlow protocol in 2008, new and more advanced generations of SDN protocols have emerged and have been introduced into transport networks, cross-haul networks, and data center (DC) or campus networks. Despite this optimistic market picture, the crude reality is that network operators are only slowly adopting basic SDN deployments. Facing this reality, it can be claimed that there is no clear path to introduce SDN in operator networks. Several barriers are currently blocking the adoption of this technology. The first SDN adoption barrier is related to network operator culture. While it is clear that network automation is needed for network operators to fully benefit from SDN adoption, to enable such automation, it is critical to define the use cases and workflows with standard interfaces that can operate in greenfield and brownfield deployments. We have observed the confluence of artificial intelligence/machine learning (ML) and 5G networks during the last few years, with ML adoption slowly evolving, but without a clear adoption path. Automation and ML are two sides of the same coin. The applicability of automated decisions in this context is uncertain when dealing with network management challenges, security, optimization, and scalability.

SDN must provide the capabilities to fulfill beyond 5G (B5G) networks' requirements. Current SDN controller solutions, such as ONOS or OpenDayLight, consist of a monolithic software core that can synchronize with other deployed SDN controllers through specific protocols. Some limitations to this current software architecture have been raised, and SDN organizations are slowly looking at possible solutions by completely redesigning SDN controllers. Research on Cloud-native architectures is being addressed at optical networks. Moreover, integration is needed for a proper integration of SDN controllers in network function virtualization (NFV) and mobile edge computing (MEC) orchestration of other technologies, such as P4/OpenFlow-based programmable switches, as well as (FPGA-based) Smart NICs, GPUs, 5G gNodeB BS.

The scope of this Special Issue includes but is not limited to the following topics:

  • Evaluation of current SDN controllers for disaggregated optical networks;
  • Cloud-native solutions for SDN control and management of optical networks;
  • SDN control of multi-layer network, e.g., optical+IP, packet-optical;
  • Latest evolution of NothBound interfaces for SDN Controllers, such as ONF Transport API, IETF TEAS models;
  • SDN-based security for optical networks;
  • SDN-based machine learning algorithms;
  • SDN-based optical network automation;
  • Integration of SDN-controlled optical networks in beyond 5G networks.

Submissions to the Special Issue should be prepared according to the MDPI Photonics journal's usual standards and will undergo the normal peer-review process.

Dr. Ricard Vilalta
Dr. Victor Lopez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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21 pages, 5158 KiB  
Article
SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models
by Elaiyasuriyan Ganesan, I-Shyan Hwang, Andrew Tanny Liem and Mohammad Syuhaimi Ab-Rahman
Photonics 2021, 8(6), 201; https://doi.org/10.3390/photonics8060201 - 04 Jun 2021
Cited by 18 | Viewed by 2926
Abstract
Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low [...] Read more.
Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques. Full article
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