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SDN Networks for Modern Communication Systems

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 29903

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Dr. Ammar Muthanna

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Guest Editor

Department of Applied Probability and Informatics, RUDN University, 117198 Moscow, Russia
Interests: Internet of Things (IoT); software-defined networking (SDN); 5G; 6G; intelligent edge
Special Issues, Collections and Topics in MDPI journals

Prof. Robert Newman

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Guest Editor

Department of Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom
Interests: internet of things,sensor networks,pervasive computing

Dr. Abdelrahman Abuarqoub

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Department of Computer Science, Middle East University, Amman, Jordan
Interests: algorithms; communication; cross-layered solutions to wireless sensor networks

Dr. Abdelhamied Ashraf Ateya

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Guest Editor

Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt
Interests: 5G wireless communications; tactile internet; vehicular networks; SDN; MEC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Software-defined networking (SDN) is a recent communication paradigm that enables the softwarization of the communication network. It comes with various benefits that make it one of the key solutions of the 5G cellular systems. SDN networks achieve high network flexibility and reliability by separating the forwarding plane and the control plane of the communication networks. The control plane may deploy a single controller or multiple controllers in distributed form. SDN enables and facilitates the implementation of another communication paradigm, which is the network function virtualization (NFV).

This Special Issue will cover the broad range of research problems associated with SDN/NFV networks. Researchers are invited to submit novel contributions in, but not limited to, the following topics:

Structure of SDN networks to assist 5G systems
Controller to controller communication
Clustering of SDN networks
Physical and logical implementation
Controller placement and allocations problems
System-level integration with 5G systems
Traffic management of SDN based networks
Performance optimization of SDN networks
Performance evaluation of current existing SDN controllers
Security issues/analysis of SDN networks
NFV techniques, methods, innovations
NFV/SDN to assist 5G network slicing
AI algorithms for assisting SDN networks

Dr. Ammar Muthanna
Prof. Robert Newman
Dr. Abdelrahman Abuarqoub
Dr. Abdelhamied Ashraf Ateya
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

SDN
5G
AI algorithms
traffic management
NFV

Published Papers (4 papers)

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Research

28 pages, 5995 KiB

Open AccessArticle

Software-Defined Networking Approaches for Link Failure Recovery: A Survey

by Jehad Ali, Gyu-min Lee, Byeong-hee Roh, Dong Kuk Ryu and Gyudong Park

Sustainability 2020, 12(10), 4255; https://doi.org/10.3390/su12104255 - 22 May 2020

Cited by 60 | Viewed by 11050

Abstract

Deployment of new optimized routing rules on routers are challenging, owing to the tight coupling of the data and control planes and a lack of global topological information. Due to the distributed nature of the traditional classical internet protocol networks, the routing rules and policies are disseminated in a decentralized manner, which causes looping issues during link failure. Software-defined networking (SDN) provides programmability to the network from a central point. Consequently, the nodes or data plane devices in SDN only forward packets and the complexity of the control plane is handed over to the controller. Therefore, the controller installs the rules and policies from a central location. Due to the central control, link failure identification and restoration becomes pliable because the controller has information about the global network topology. Similarly, new optimized rules for link recovery can be deployed from the central point. Herein, we review several schemes for link failure recovery by leveraging SDN while delineating the cons of traditional networking. We also investigate the open research questions posed due to the SDN architecture. This paper also analyzes the proactive and reactive schemes in SDN using the OpenDayLight controller and Mininet, with the simulation of application scenarios from the tactical and data center networks. Full article

(This article belongs to the Special Issue SDN Networks for Modern Communication Systems)

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16 pages, 1279 KiB

Open AccessArticle

Enabling Packet Classification with Low Update Latency for SDN Switch on FPGA

by Chenglong Li, Tao Li, Junnan Li, Zilin Shi and Baosheng Wang

Sustainability 2020, 12(8), 3068; https://doi.org/10.3390/su12083068 - 11 Apr 2020

Cited by 9 | Viewed by 2366

Abstract

Field Programmable Gate Array (FPGA) is widely used in real-time network processing such as Software-Defined Networking (SDN) switch due to high performance and programmability. Bit-Vector (BV)-based approaches can implement high-performance multi-field packet classification, on FPGA, which is the core function of the SDN switch. However, the SDN switch requires not only high performance but also low update latency to avoid controller failure. Unfortunately, the update latency of BV-based approaches is inversely proportional to the number of rules, which means can hardly support the SDN switch effectively. It is reasonable to split the ruleset into sub-rulesets that can be performed in parallel, thereby reducing update latency. We thus present SplitBV for the efficient update by using several distinguishable exact-bits to split the ruleset. SplitBV consists of a constrained recursive algorithm for selecting the bit positions that can minimize the latency and a hybrid lookup pipeline. It can achieve a significant reduction in update latency with negligible memory growth and comparable high performance. We implement SplitBV and evaluate its performance by simulation and FPGA prototype. Experimental results show that our approach can reduce 73% and 36% update latency on average for synthetic 5-tuple rules and OpenFlow rules respectively. Full article

(This article belongs to the Special Issue SDN Networks for Modern Communication Systems)

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20 pages, 1120 KiB

Open AccessArticle

Intent-Based End-to-End Network Service Orchestration System for Multi-Platforms

by Adeel Rafiq, Asif Mehmood, Talha Ahmed Khan, Khizar Abbas, Muhammad Afaq and Wang-Cheol Song

Sustainability 2020, 12(7), 2782; https://doi.org/10.3390/su12072782 - 01 Apr 2020

Cited by 38 | Viewed by 4746

Abstract

On-demand service is the main feature of the 5G network, and Network Function Virtualization (NFV) provides it by virtualizing the existing 5G network infrastructure. NFV crafts various virtual networks on a shared physical network, but one of the core challenges in future 5G networks is to automate the modeling of Virtualized Network Functions (VNFs) and end-to-end Network Service (NS) orchestration with less human interaction. Traditionally, the descriptor of VNF and NS is created manually, which requires expert-level skills. This manual approach has a big threat of human error, which can be avoided by using the Intent-Based Networking (IBN) approach. The IBN approach eliminates the requirement of expertise for designing VNFs and NS by taking users’ intentions as an input. In this paper, the proposed system presents the Intent Management System for VNF modeling and end-to-end NS orchestration for multi-platforms. This system takes the high-level information related to a specific service, configures it accordingly, and converts it into the selected platform. The proposed system is tested using Mobile Central Office Re-architected as Data Center (M-CORD) and Open-Source Management and Orchestration (OSM) orchestrators. The results section shows that the proposed system reduces the effort of the end-user in creating network slices and provides seamless end-to-end service orchestration. Full article

(This article belongs to the Special Issue SDN Networks for Modern Communication Systems)

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16 pages, 1133 KiB

Open AccessArticle

Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models

by Huseyin Polat, Onur Polat and Aydin Cetin

Sustainability 2020, 12(3), 1035; https://doi.org/10.3390/su12031035 - 01 Feb 2020

Cited by 143 | Viewed by 10528

Abstract

Software Defined Networking (SDN) offers several advantages such as manageability, scaling, and improved performance. However, SDN involves specific security problems, especially if its controller is defenseless against Distributed Denial of Service (DDoS) attacks. The process and communication capacity of the controller is overloaded when DDoS attacks occur against the SDN controller. Consequently, as a result of the unnecessary flow produced by the controller for the attack packets, the capacity of the switch flow table becomes full, leading the network performance to decline to a critical threshold. In this study, DDoS attacks in SDN were detected using machine learning-based models. First, specific features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Then, a new dataset was created using feature selection methods on the existing dataset. Feature selection methods were preferred to simplify the models, facilitate their interpretation, and provide a shorter training time. Both datasets, created with and without feature selection methods, were trained and tested with Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) classification models. The test results showed that the use of the wrapper feature selection with a KNN classifier achieved the highest accuracy rate (98.3%) in DDoS attack detection. The results suggest that machine learning and feature selection algorithms can achieve better results in the detection of DDoS attacks in SDN with promising reductions in processing loads and times. Full article

(This article belongs to the Special Issue SDN Networks for Modern Communication Systems)

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