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Control Methods to Improve the Sustainability and Energy Efficiency of 5G and Optical Networks

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 16261

Special Issue Editors

Dr. Ramón J. Durán Barroso

E-Mail Website
Guest Editor
Paseo de Belén, Universidad de Valladolid, 15, 47011 Valladolid, Spain
Interests: optical networks; IoT; artificial intelligence; SDN; network virtualization
Special Issues, Collections and Topics in MDPI journals
Dr. Ignacio de Miguel

E-Mail Website
Guest Editor
Universidad de Valladolid. Paseo de Belén, 15, 47011, Valladolid, Spain
Interests: Machine Learning; Optical Networks; Network Control; Energy efficiency
Special Issues, Collections and Topics in MDPI journals
Dr. Pouria Sayyad Khodashenas

E-Mail Website
Guest Editor
i2CAT Foundation. c/Gran Capità, 2, 08034, Barcelona, Spain
Interests: 5G Networks; Optical Networks; Network Softwarization; Orchestration
Dr. F. Javier Moreno Muro

E-Mail Website
Guest Editor
Universidad Politécnica de Cartagena, Cuartel de Antigones, Plaza del Hospital 1, 30202, Cartagena, Spain
Interests: Optical Networks; Network Planning; SDN; Network Virtualization

Special Issue Information

Dear Colleagues,

Current networking and computing infrastructures are facing significant challenges in order to support the requirements of emerging paradigms like the Internet of Things (IoT), Industry 4.0 or Tactile Internet, as they impose stringent requirements on key performance indicators like scalability, latency or bandwidth.

The scope of this special issue is related to the key ingredients for providing a solution to that challenge, with ensuring the sustainability and energy efficiency of ICT infrastructures. Thus, topics accepted in the special issue include (but are not limited to) the following:

  • Power consumption models and techno-economic models for optical networks and 5G
  • Green optical and 5G networks
  • Dimensioning, control and deployment of future optical networks.
  • Efficient convergence of 5G radio and optical infrastructures
  • Software-Defined Networking (SDN) and Network Function Virtualization in energy efficient networks.
  • Network slicing in optical and/or 5G networks
  • Artificial intelligence for efficient planning and/or control of optical and/or 5G networks.
  • Distributed computing based on Fog/MEC/Cloud hierarchy
  • Holistic resource orchestration and management including Fog/MEC/Cloud computing resources.
  • Consumption-aware resource management and orchestration

Dr. Ramón J. Durán Barroso
Dr. Ignacio de Miguel
Dr. Pouria Sayyad Khodashenas
Dr. F. Javier Moreno Muro
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. Sustainability is an international peer-reviewed open access semimonthly 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.

Keywords

  • optical networks
  • 5G
  • IoT
  • artificial intelligence
  • SDN
  • NFV
  • network planning
  • network control
  • energy efficiency
  • green networking

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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23 pages, 1775 KiB  
Article
Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment
by Sushil Kumar Singh, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan and Jong Hyuk Park
Sustainability 2020, 12(15), 6250; https://doi.org/10.3390/su12156250 - 3 Aug 2020
Cited by 49 | Viewed by 6535
Abstract
Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a [...] Read more.
Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation. Full article
(This article belongs to the Special Issue Control Methods to Improve the Sustainability and Energy Efficiency of 5G and Optical Networks)
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23 pages, 5634 KiB  
Article
Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture
by Ricardo S. Alonso, Inés Sittón-Candanedo, Roberto Casado-Vara, Javier Prieto and Juan M. Corchado
Sustainability 2020, 12(14), 5706; https://doi.org/10.3390/su12145706 - 15 Jul 2020
Cited by 23 | Viewed by 4725
Abstract
The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the [...] Read more.
The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the computing and storage resources used in the Cloud. In this regard, Edge Computing can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered in network edge before being sent to the Cloud, resulting in shorter response times and providing a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover, there is a growing trend to share physical network resources and costs through Network Function Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks (SDNs) are used to reconfigure the network dynamically according to the necessities during time. For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques, can be employed to manage virtual data flows in networks. In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller. Full article
(This article belongs to the Special Issue Control Methods to Improve the Sustainability and Energy Efficiency of 5G and Optical Networks)
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20 pages, 6443 KiB  
Article
An Energy-Efficient Distributed Dynamic Bandwidth Allocation Algorithm for Passive Optical Access Networks
by Hamzeh Khalili, David Rincón, Sebastià Sallent and José Ramón Piney
Sustainability 2020, 12(6), 2264; https://doi.org/10.3390/su12062264 - 13 Mar 2020
Cited by 20 | Viewed by 4266
Abstract
The rapid deployment of passive optical access networks (PONs) increases the global energy consumption of networking infrastructure. This paper focuses on the minimization of energy consumption in Ethernet PONs (EPONs). We present an energy-efficient, distributed dynamic bandwidth allocation (DBA) algorithm able to power [...] Read more.
The rapid deployment of passive optical access networks (PONs) increases the global energy consumption of networking infrastructure. This paper focuses on the minimization of energy consumption in Ethernet PONs (EPONs). We present an energy-efficient, distributed dynamic bandwidth allocation (DBA) algorithm able to power off the transmitter and receiver of an optical network unit (ONU) when there is no upstream or downstream traffic. Our main contribution is combining the advantages of a distributed DBA (namely, a smaller packet delay compared to centralized DBAs, due to less time being needed to allocate the transmission slot) with energy saving features (that come at a price of longer delays due to the longer queue waiting times when transmitters are switched off). The proposed algorithm analyzes the queue size of the ONUs in order to switch them to doze/sleep mode when there is no upstream/downstream traffic in the network, respectively. Our results show that we minimized the ONU energy consumption across a wide range of network loads while keeping delay bounded. Full article
(This article belongs to the Special Issue Control Methods to Improve the Sustainability and Energy Efficiency of 5G and Optical Networks)
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