Emerging Networks and Systems for Edge Computing

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 12152

Special Issue Editors


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Guest Editor
Department of Computer Science, Loughborough University, Loughborough, UK
Interests: edge computing; federated service chaining; programmable data plane

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Guest Editor
Information Technology Center, The University of Tokyo, Tokyo, Japan
Interests: system software; network systems; secure systems
Department of Computer Science, William Paterson University, Wayne, NJ 07470, USA
Interests: cybersecurity; network security; machine learning; vehicular networks; wireless communication; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rise of edge computing has fundamentally changed the research landscape regarding computer systems and networks. Computation taking place closer to end users means computation tasks will need to work across heterogeneous systems and networks owned by many different stakeholders, whose different operating objectives will prevent the delivery of seamless network services across network/operator boundaries, which are needed to support efficient edge computing. Therefore, we anticipate that there will be a strong need for federated access to resources and networks. In the meantime, research and innovation is also needed to address the business drive for low-latency computation, secure systems for protecting systems and networks, privacy-preserving data sharing techniques, etc., needed for edge computing to be useful and trustworthy.

The aim of this Special Issue is to capture and present research from a wide range of emerging topics related to the modeling, design, and analysis of networks and systems for edge computing. To this end, we solicit original papers in areas including, but not limited to:

  • Opportunistic computing; 
  • Distributed algorithms;
  • Network federation;
  • Service chaining for the edge;
  • Distributed and decentralized system configuration and management;
  • Systems and networks co-design;
  • Edge AI/machine learning;
  • Simulation and performance evaluation;
  • Network programmability;
  • Programmable data plane for edge computing;
  • Secure networked system design;
  • Cybersecurity for the edge;
  • Vehicular (including unmanned aerial vehicle) networks;
  • Network policy enforcement and validation;
  • Opportunistic networking

Dr. Posco Fung Po Tso
Dr. Takahiro Shinagawa
Dr. Kiho Lim
Guest Editors

Manuscript Submission Information

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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. Network 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 1000 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

  • Federated networks
  • network programmability
  • stateful data plane
  • system and network security
  • IoT

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Published Papers (4 papers)

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Research

16 pages, 407 KiB  
Article
Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology
by Pedro Juan Roig, Salvador Alcaraz, Katja Gilly, Cristina Bernad and Carlos Juiz
Network 2023, 3(1), 142-157; https://doi.org/10.3390/network3010007 - 30 Jan 2023
Cited by 3 | Viewed by 2455
Abstract
Data center organization and optimization presents the opportunity to try and design systems with specific characteristics. In this sense, the combination of artificial intelligence methodology and sustainability may lead to achieve optimal topologies with enhanced feature, whilst taking care of the environment by [...] Read more.
Data center organization and optimization presents the opportunity to try and design systems with specific characteristics. In this sense, the combination of artificial intelligence methodology and sustainability may lead to achieve optimal topologies with enhanced feature, whilst taking care of the environment by lowering carbon emissions. In this paper, a model for a field monitoring system has been proposed, where an edge data center topology in the form of a redundant ring has been designed for redundancy purposes to join together nodes spread apart. Additionally, a formal algebraic model of such a design has been exposed and verified. Full article
(This article belongs to the Special Issue Emerging Networks and Systems for Edge Computing)
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15 pages, 2102 KiB  
Article
An Uncertainty-Driven Proactive Self-Healing Model for Pervasive Applications
by Maria Papathanasaki, Panagiotis Fountas and Kostas Kolomvatsos
Network 2022, 2(4), 568-582; https://doi.org/10.3390/network2040033 - 25 Oct 2022
Viewed by 1652
Abstract
The ever-increasing demand for services of end-users in the Internet of Things (IoT) often causes great congestion in the nodes dedicated to serving their requests. Such nodes are usually placed at the edge of the network, becoming the intermediates between the IoT infrastructure [...] Read more.
The ever-increasing demand for services of end-users in the Internet of Things (IoT) often causes great congestion in the nodes dedicated to serving their requests. Such nodes are usually placed at the edge of the network, becoming the intermediates between the IoT infrastructure and Cloud. Edge nodes offer many advantages when adopted to perform processing activities that are realized close to end-users, limiting the latency in the provision of responses. In this article, we attempt to solve the problem of the potential overloading of edge nodes by proposing a mechanism that always keeps free space in their queue to host high-priority processing tasks. We introduce a proactive, self-healing mechanism that utilizes the principles of Fuzzy Logic, in combination with a non-parametric statistical method that reveals the trend of nodes’ loads as depicted by the incoming tasks and their capability to serve them in the minimum possible time. Through our approach, we manage to ensure the uninterrupted service of high-priority tasks, taking into consideration the demand for tasks as well. Based on this approach, we ensure the fastest possible delivery of results to the requestors while keeping the latency for serving high-priority tasks at the lowest possible levels. A set of experimental scenarios is adopted to evaluate the performance of the suggested model by presenting the corresponding numerical results. Full article
(This article belongs to the Special Issue Emerging Networks and Systems for Edge Computing)
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19 pages, 848 KiB  
Article
Quality of Experience Experimentation Prediction Framework through Programmable Network Management
by Ahmed Osama Basil Al-Mashhadani, Mu Mu and Ali Al-Sharbaz
Network 2022, 2(4), 500-518; https://doi.org/10.3390/network2040030 - 8 Oct 2022
Viewed by 2265
Abstract
Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has [...] Read more.
Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE. Full article
(This article belongs to the Special Issue Emerging Networks and Systems for Edge Computing)
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15 pages, 1849 KiB  
Article
A Study of 5G Edge-Central Core Network Split Options
by Marius Corici, Pousali Chakraborty and Thomas Magedanz
Network 2021, 1(3), 354-368; https://doi.org/10.3390/network1030020 - 20 Dec 2021
Cited by 7 | Viewed by 4431
Abstract
With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks [...] Read more.
With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments. Full article
(This article belongs to the Special Issue Emerging Networks and Systems for Edge Computing)
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