Convergence of Edge Computing and Next Generation Networking

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

Deadline for manuscript submissions: 20 October 2024 | Viewed by 998

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: edge computing; cloud-to-thing-continuum; industrial IoT
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Guest Editor
Head of Technology Transformation, Standardization and IPR at TIM, 4455 Roma, Italy
Interests: telecommunication networks
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Guest Editor
Faculty of Applied Engineering, Universiteit Antwerpen, 2000 Antwerpen, Belgium
Interests: 5G advanced heterogeneous dense cells architectures; elastic and flexible future wireless networks and its integration and impact on optical networks; IoT clustering; virtualization; provisioning and dynamic resource allocation towards dynamic converged networks; vehicular networks, mobility and handovering within smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fulfilling the edge computing promise of zero (low) latency, high bandwidth communication, low energy consumption, and (potentially) enhanced data security and privacy, it is expected that there will be a significant push towards edge application/service deployment. Edge computing not only offers developments in the network architecture, but also presents a potential for innovation in service patterns, assuming that application/service Quality of Service (QoS) constraints can be fulfilled along its lifetime. From a network organization viewpoint, several hierarchical layers of edge nodes with different capabilities can be deployed, thus distributing the resources toward the end-user to support the execution of applications and their data; this will generate a more fluid edge network model. On the other hand, with the rapid development of information technology, the network is experiencing unprecedented transformations, as both the number of connections and the volume of data are extensively increased. To ensure that the networks are suitable for such ever-growing diverse needs, many novel networking technologies have been proposed, covering both the access and the backbone network. As an example, the relatively recent Open Radio Access Network (O-RAN) initiative, which targets an open, virtualized and interoperable RAN, advocates for the integration of AI and machine learning to enable smarter network management; this will lead to more efficient resource utilization, predictive maintenance, and enhanced security features. Software-defined Networking (SDN) decouples the control plane from the data plane to enable more flexible and customized network flow control. The Information-Centric Network (ICN) evolves the current Internet infrastructure from the host-centric paradigm to the data or service-name-centric paradigm. These newly emerging networking technologies are already widely regarded as the key enabling technologies in future networks.

Service provisioning at the edge, however, is associated with numerous challenges that are unique to distributed edge cloud environments. A fundamental limitation of the approach is that in contrast to traditional cloud platforms and data centers, edge clouds have limited resources and may not always be able to satisfy application demands for resources. It is clear that only introducing support for the execution of applications at edge nodes (e.g., through containerization) is not sufficient. The seamless integration of all levels of the infrastructure and novel management approaches that coordinate and orchestrates its (virtualized) resources vertically and horizontally, while ensuring QoS, is of paramount importance.

This Special Issue aims to compile novel research on algorithmic, architectural, and system issues, as well as on experimental aspects that advance the state of the art in the design of integrated resource management in future decentralized edge networks. Prospective authors are invited to submit original, high-quality contributions in areas including, but not limited to, the following:

  • AI techniques for resource management and distributed control;
  • Multi-scale, closed-loop control techniques for edge-cloud networks;
  • Analysis of fundamental trade-offs in edge systems, including metrics such as energy efficiency, latency, overhead, cost, among others;
  • Algorithmic approaches for end-to-end network slicing and SLA assurance;
  • Approaches for cross-domain, cross-edge resource federation and trustworthy cooperation;
  • Data management in decentralized and federated edge networks;
  • Application acceleration use cases, including the metaverse;
  • Novel use cases and applications for converged edge-cloud networks;
  • Security analysis and solutions for decentralized edge networks;
  • Data-driven techniques to enhance network security;
  • Design and optimization of edge-cloud solutions for private networks;
  • Beyond containerization workload management models;
  • Large-scale testbed design and trial.

You may choose our Joint Special Issue in Future Internet.

Dr. Armir Bujari
Dr. Gabriele Elia
Prof. Dr. Johann M. Marquez-Barja
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.

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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

  • network management
  • federated edge networks
  • network programmability
  • cloud continuum
  • ai for the network
  • network security
  • network slicing
  • network intelligence

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Published Papers (1 paper)

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14 pages, 1355 KiB  
Article
Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
by Ali Al-Allawee, Pascal Lorenz and Alhamza Munther
Network 2024, 4(3), 390-403; https://doi.org/10.3390/network4030018 - 6 Sep 2024
Viewed by 711
Abstract
Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been [...] Read more.
Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been observed between IoV and edge computing to support smooth mobility and the use of local resources. However, vehicle travel, especially due to high-speed movement and intersections, can result in IoV devices losing connection and/or processing with high latency. This paper proposes a Cluster Collaboration Vehicular Edge Computing (CCVEC) framework that aims to guarantee and enhance the connectivity between vehicle sensors and the cloud by utilizing the edge computing paradigm in the middle. The objectives are achieved by utilizing the cluster management strategies deployed between cloud and edge computing servers. The framework is implemented in OpenStack cloud servers and evaluated by measuring the throughput, latency, and memory parameters in two different scenarios. The results obtained show promising indications in terms of latency (approximately 390 ms of the ideal status) and throughput (30 kB/s) values, and thus appears acceptable in terms of performance as well as memory. Full article
(This article belongs to the Special Issue Convergence of Edge Computing and Next Generation Networking)
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