Special Issue "The Future of Network Softwarization: A Network Function Virtualization Approach"

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editor

Dr. Aris Leivadeas
E-Mail Website
Guest Editor
Department of Software and Information Technology Engineering,École de technologie supérieure,Montreal, QC, H3C 1K3 Canada
Interests: Virtualization; Resource Allocation; Optimization Algorithms; Network Management and Orchestration; Wireless Communications; IoT

Special Issue Information

Dear Colleagues,

Network function virtualization (NFV) along with service function chaining (SFC) have played a significant role in softwarizing the network infrastructure. In particular, both technologies have revolutionized the way network functions and services are offered, promoting significant agility and cost reductions.

Even though NFV solutions have been extensively used and analyzed regarding cloud- and enterprise-based scenarios, new requirements, constraints, and opportunities arise from the advent of new technologies such as Internet of Things (IoT) and 5G. The key ingredient to the success story of IoT and 5G will be the softwarization of network infrastructure, rendering NFV one of the foundation components of this network evolution.

To this end, this Special Issue is soliciting conceptual, theoretical, and experimental contributions to a set of currently unresolved challenges in the area of IoT and 5G-aware NFV platforms. The topics of interest include but are not limited to:

  • End-to-end service function chaining;
  • Resource allocation for an IoT/edge/cloud interplay;
  • Management and orchestration in an end-to-end NFV deployment solution;
  • Network slicing for 5G;
  • Security assurance in IoT and 5G through NFV;
  • Performance analysis of different virtualized network function (VNF) deployments (VMs, containers, micro-VMs) and their suitability in IoT and 5G platforms;
  • Impact of IoT and 5G traffic behavior in the performance of NFV;
  • Energy-sustained development and NFV;
  • Testbeds and experimental facilities reports;
  • Business and techno-economic opportunities.

Dr. Aris Leivadeas
Guest Editor

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 papers will be 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. Informatics is an international peer-reviewed open access quarterly 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 1400 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

  • NFV
  • service function chaining
  • IoT
  • 5G
  • edge/fog computing
  • network slicing
  • VNF placement for IoT
  • NFV performance optimization
  • NFV management and orchestration for IoT and 5G
  • security aspects of NFV
  • energy-efficient NFV

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessFeature PaperArticle
On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds
Informatics 2021, 8(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8010013 - 26 Feb 2021
Viewed by 388
Abstract
With the advent of 5G verticals and the Internet of Things paradigm, Edge Computing has emerged as the most dominant service delivery architecture, placing augmented computing resources in the proximity of end users. The resource orchestration of edge clouds relies on the concept [...] Read more.
With the advent of 5G verticals and the Internet of Things paradigm, Edge Computing has emerged as the most dominant service delivery architecture, placing augmented computing resources in the proximity of end users. The resource orchestration of edge clouds relies on the concept of network slicing, which provides logically isolated computing and network resources. However, though there is significant progress on the automation of the resource orchestration within a single cloud or edge cloud datacenter, the orchestration of multi-domain infrastructure or multi-administrative domain is still an open challenge. Towards exploiting the network service marketplace at its full capacity, while being aligned with ETSI Network Function Virtualization architecture, this article proposes a novel Blockchain-based service orchestrator that leverages the automation capabilities of smart contracts to establish cross-service communication between network slices of different tenants. In particular, we introduce a multi-tier architecture of a Blockchain-based network marketplace, and design the lifecycle of the cross-service orchestration. For the evaluation of the proposed approach, we set up cross-service communication in an edge cloud and we demonstrate that the orchestration overhead is less than other cross-service solutions. Full article
Show Figures

Figure 1

Open AccessArticle
A Survey of Deep Learning for Data Caching in Edge Network
Informatics 2020, 7(4), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040043 - 13 Oct 2020
Cited by 2 | Viewed by 909
Abstract
The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user [...] Read more.
The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching. Full article
Show Figures

Figure 1

Open AccessFeature PaperArticle
VNF Chaining Performance Characterization under Multi-Feature and Oversubscription Using SR-IOV
Informatics 2020, 7(3), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7030033 - 14 Sep 2020
Viewed by 880
Abstract
Network Function Virtualization (NFV) has revolutionized the way network services are offered, leading Enterprise and Service Providers to increasingly adapt their portfolio of network products in order to reap the benefits of flexible network service deployment and cost reduction promises. With this method, [...] Read more.
Network Function Virtualization (NFV) has revolutionized the way network services are offered, leading Enterprise and Service Providers to increasingly adapt their portfolio of network products in order to reap the benefits of flexible network service deployment and cost reduction promises. With this method, network services are offered in the form of software images instead of dedicated hardware. However, NFV presents several challenges, including standard networking challenges (e.g., security, resilience, and availability), management and orchestration challenges, resource allocation challenges, and performance trade-off challenges of using standard x86 servers instead of dedicated and proprietary hardware. The first three challenges are typical challenges found in virtualization environments and have been extensively addressed in the literature. However, the performance trade-off challenge can be the most impactful when offering networking services, negatively affecting the throughput and delay performance achieved. Thus, in this paper, we investigate and propose several configurations on a virtualized system for increasing the performance in terms of throughput and delay while chaining multiple virtual network functions (VNFs) in case of an undersubscribed and oversubscribed system, where the resource demands exceeds the physical resource capacity. Specifically, we use the Single Root Input Output Virtualization (SR-IOV) as our Input/Output (I/O) technology, and analyze the attainable throughput and delay when running multiple chained VNFs in a standard x86 server under various resource footprints and network features configurations. We show that the system throughput and delay in a multi-chained environment, offering multiple features, and under oversubscription can affect the overall performance of VNFs. Full article
Show Figures

Figure 1

Back to TopTop