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Cognitive Software Defined Networking and Network Function Virtualization and Applications

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 11066

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Special Issue Editors

Dr. Sachin Sharma

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Guest Editor

School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
Interests: cloud computing; OpenFlow; software-defined networking
Special Issues, Collections and Topics in MDPI journals

Dr. Avishek Nag

E-Mail Website
Guest Editor

School of Electrical and Electronic Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: 5G; AI in networks; optimization; optical networks; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A major development in computer networking is the emergence of Software Defined Networking (SDN) and Network Function Virtualization (NFV). The goal of SDN is to provide a centralized, programmable control plane that is decoupled from the data plane of network devices, while the goal of NFV is to virtualize network functions (such as network address translation, firewall, and intrusion detection) that are now being implemented by proprietary, dedicated hardware. In future networks, Artificial Intelligence (AI) and Machine Learning (ML) will be key technologies to automate network operations and enhance customer experience. AI and ML will help in making intelligent decisions regarding performance guarantees, energy saving, resource optimizations and security. This Issue is dedicated to cognitive software defined networking and network function virtualization. New cognitive techniques enabled by Artificial Intelligence (AI)/Machine Learning (ML) will be a pivoting point for enhancing automation in network operations. In addition, AI can help in decomposing large network functions into micro network functions (e.g., microservices). Currently, many of the research challenges behind SDN/NFV are or have been widely investigated in several projects all around the globe. AI-based software based networking, autonomic networking, policy-based network management, network slicing, and other related research areas will be key underlying technologies in future networks. This Special Issue seeks contributions from the above area of research.

Dr. Sachin Sharma
Dr. Avishek Nag
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. Future Internet 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 1600 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

Software Defined Networking (SDN)
Network Function Virtualization (NFV)
Artificial Intelligence and Machine Learning
5G
Internet of Things
Cloud Computing

Published Papers (5 papers)

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Editorial

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3 pages, 171 KiB

Open AccessEditorial

Cognitive Software Defined Networking and Network Function Virtualization and Applications

by Sachin Sharma and Avishek Nag

Future Internet 2023, 15(2), 78; https://doi.org/10.3390/fi15020078 - 17 Feb 2023

Viewed by 1445

Abstract

The emergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the Internet. Using SDN, network devices can be controlled from a centralized, programmable control plane that is decoupled from their data plane, whereas with NFV, network functions (such as network address translation, firewall, and intrusion detection) can be virtualized instead of being implemented on proprietary hardware. In addition, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key to automating network operations and enhancing customer service. Many of the challenges behind SDN and NFV are currently being investigated in several projects all over the world using AI and ML techniques, such as AI- and software-based networking, autonomic networking, and policy-based network management. Contributions to this Special Issue come from the above areas of research. Following a rigorous review process, four excellent articles were accepted that address and go beyond many of the challenges mentioned above. Full article

(This article belongs to the Special Issue Cognitive Software Defined Networking and Network Function Virtualization and Applications)

Research

Jump to: Editorial

13 pages, 773 KiB

Open AccessArticle

Scheduling for Media Function Virtualization

by Gourav Prateek Sharma, Wouter Tavernier, Didier Colle and Mario Pickavet

Future Internet 2021, 13(7), 167; https://doi.org/10.3390/fi13070167 - 28 Jun 2021

Cited by 1 | Viewed by 2173

Abstract

Broadcasters are building studio architectures based on commercial off-the-shelf (COTS) IT hardware because of advantages such as cost reduction, ease of management, and upgradation. Media function virtualization (MFV) leverages IP networking to transport media streams between virtual media functions (VMFs), where they are processed. Media service deployment in an MFV environment entails solving the VMF-FG scheduling problem to ensure that the required broadcast quality guarantees are fulfilled. In this paper, we formulate the VMF-FG scheduling problem and propose a greedy-based algorithm to solve it. The evaluation of the algorithm is carried in terms of the end-to-end delay and VMF queuing delay. Moreover, the importance of VMF-FG decomposition in upgradation to higher-quality formats is also highlighted. Full article

(This article belongs to the Special Issue Cognitive Software Defined Networking and Network Function Virtualization and Applications)

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25 pages, 2870 KiB

Open AccessArticle

Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning

by Swarna Bindu Chetty, Hamed Ahmadi, Sachin Sharma and Avishek Nag

Future Internet 2021, 13(3), 82; https://doi.org/10.3390/fi13030082 - 23 Mar 2021

Cited by 5 | Viewed by 2499

Abstract

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear programming) for deploying Virtualized Network Functions (VNFs) under several Quality-of-Service (QoS) constraints such as latency, memory, CPU, and failure recovery requirements. More importantly, the failure recovery requirements are focused on the node-outage problem where outage can be either due to a disaster or unavailability of network topology information (e.g., due to proprietary and ownership issues). In DRL, we adopt a Deep Q-Learning (DQL) based algorithm where the primary network estimates the action-value function Q, as well as the predicted Q, highly causing divergence in Q-value’s updates. This divergence increases for the larger-scale action and state-space causing inconsistency in learning, resulting in an inaccurate output. Thus, to overcome this divergence, our work has adopted a well-known approach, i.e., introducing Target Neural Networks and Experience Replay algorithms in DQL. The constructed model is simulated for two real network topologies—Netrail Topology and BtEurope Topology—with various capacities of the nodes (e.g., CPU core, VNFs per Core), links (e.g., bandwidth and latency), several VNF Forwarding Graph (VNF-FG) complexities, and different degrees of the nodal outage from 0% to 50%. We can conclude from our work that, with the increase in network density or nodal capacity or VNF-FG’s complexity, the model took extremely high computation time to execute the desirable results. Moreover, with the rise in complexity of the VNF-FG, the resources decline much faster. In terms of the nodal outage, our model provided almost 70–90% Service Acceptance Rate (SAR) even with a 50% nodal outage for certain combinations of scenarios. Full article

(This article belongs to the Special Issue Cognitive Software Defined Networking and Network Function Virtualization and Applications)

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17 pages, 472 KiB

Open AccessArticle

Smart Site Diversity for a High Throughput Satellite System with Software-Defined Networking and a Virtual Network Function

by Gandhimathi Velusamy and Ricardo Lent

Future Internet 2020, 12(12), 225; https://doi.org/10.3390/fi12120225 - 07 Dec 2020

Cited by 4 | Viewed by 2588

Abstract

High Throughput Satellite (HTS) systems aim to push data rates to the order of Terabit/s, making use of Extremely High Frequencies (EHF) or free-space optical (FSO) in the feeder links. However, one challenge that needs to be addressed is that the use of such high frequencies makes the feeder links vulnerable to atmospheric conditions, which can effectively disable channels at times or temporarily increases the bit error rates. One way to cope with the problem is to introduce site diversity and to forward the data through the gateways not affected, or at least less constrained, by adverse conditions. In this paper, a virtual network function (VNF) introduced through reinforcement learning defines a smart routing service for an HTS system. Experiments were conducted on an emulated ground-satellite system in CloudLab, testing a VNF implementation of the approach with software-defined networking virtual switches, which indicate the expected performance of the proposed method. Full article

(This article belongs to the Special Issue Cognitive Software Defined Networking and Network Function Virtualization and Applications)

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13 pages, 1661 KiB

Open AccessArticle

Proposal and Investigation of an Artificial Intelligence (AI)-Based Cloud Resource Allocation Algorithm in Network Function Virtualization Architectures

by Vincenzo Eramo, Francesco Giacinto Lavacca, Tiziana Catena and Paul Jaime Perez Salazar

Future Internet 2020, 12(11), 196; https://doi.org/10.3390/fi12110196 - 13 Nov 2020

Cited by 8 | Viewed by 2092

Abstract

The high time needed to reconfigure cloud resources in Network Function Virtualization network environments has led to the proposal of solutions in which a prediction based-resource allocation is performed. All of them are based on traffic or needed resource prediction with the minimization of symmetric loss functions like Mean Squared Error. When inevitable prediction errors are made, the prediction methodologies are not able to differently weigh positive and negative prediction errors that could impact the total network cost. In fact if the predicted traffic is higher than the real one then an over allocation cost, referred to as over-provisioning cost, will be paid by the network operator; conversely, in the opposite case, Quality of Service degradation cost, referred to as under-provisioning cost, will be due to compensate the users because of the resource under allocation. In this paper we propose and investigate a resource allocation strategy based on a Long Short Term Memory algorithm in which the training operation is based on the minimization of an asymmetric cost function that differently weighs the positive and negative prediction errors and the corresponding over-provisioning and under-provisioning costs. In a typical traffic and network scenario, the proposed solution allows for a cost saving by 30% with respect to the case of solution with symmetric cost function. Full article

(This article belongs to the Special Issue Cognitive Software Defined Networking and Network Function Virtualization and Applications)

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