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Cloud-Edge Continuum in 5G Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 3776

Special Issue Editor


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Guest Editor
Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: Internet of Things; smart grid and energy saving; energy saving networks; 5G; 6G

Special Issue Information

Dear Colleagues,

Networking and computing are undergoing a major shift toward cloud computing.

Cloud computing is now being adopted by end-user devices on wired and wireless networks such as "fog computing". With fog computing, cloud capability is extended through nodes and IoT gateways to provide compute, storage, and networking services. Technology uses multiple endpoints to process data closer to the end user, which is one of the major advantages. A network can be viewed as an overall picture based on the results of numerous data points, which is both more scalable and more accurate than edge computing.

In future technological developments such as 5G networks and the Internet of Things (IoT), fog computing (FC) may be a promising addition to Internet architecture. A large number of sensors generate continuous sensory information via these advanced technologies that are integrated into Internet architectures.

Special emphasis is placed on identifying the required services and network-oriented features required to build a future enterprise network using fog computing and cloud computing as enablers for 5G networks. Potential topics include, but are not limited to, the following:

  • network storage,
  • Internet of Things (IoT);
  • heterogeneous 5G mobile services;
  • fog computing;
  • cognitive radio networks;
  • software-defined networking;
  • mobile cloud computing.

Prof. Dr. Gyu Myoung Lee
Guest Editor

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Keywords

  • cloud edge computing
  • 5G
  • Internet of Things

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Published Papers (2 papers)

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Research

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24 pages, 3029 KiB  
Article
Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
by Andres F. Ocampo, Mah-Rukh Fida, Ahmed Elmokashfi and Haakon Bryhni
Sensors 2024, 24(7), 2365; https://doi.org/10.3390/s24072365 - 8 Apr 2024
Cited by 1 | Viewed by 1787
Abstract
Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to [...] Read more.
Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU’s NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments. Full article
(This article belongs to the Special Issue Cloud-Edge Continuum in 5G Networks)
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Review

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60 pages, 1381 KiB  
Review
A Survey on Reduction of Energy Consumption in Fog Networks—Communications and Computations
by Bartosz Kopras, Filip Idzikowski and Hanna Bogucka
Sensors 2024, 24(18), 6064; https://doi.org/10.3390/s24186064 - 19 Sep 2024
Viewed by 1454
Abstract
Fog networking has become an established architecture addressing various applications with strict latency, jitter, and bandwidth constraints. Fog Nodes (FNs) allow for flexible and effective computation offloading and content distribution. However, the transmission of computational tasks, the processing of these tasks, and finally [...] Read more.
Fog networking has become an established architecture addressing various applications with strict latency, jitter, and bandwidth constraints. Fog Nodes (FNs) allow for flexible and effective computation offloading and content distribution. However, the transmission of computational tasks, the processing of these tasks, and finally sending the results back still incur energy costs. We survey the literature on fog computing, focusing on energy consumption. We take a holistic approach and look at energy consumed by devices located in all network tiers from the things tier through the fog tier to the cloud tier, including communication links between the tiers. Furthermore, fog network modeling is analyzed with particular emphasis on application scenarios and the energy consumed for communication and computation. We perform a detailed analysis of model parameterization, which is crucial for the results presented in the surveyed works. Finally, we survey energy-saving methods, putting them into different classification systems and considering the results presented in the surveyed works. Based on our analysis, we present a classification and comparison of the fog algorithmic models, where energy is spent on communication and computation, and where delay is incurred. We also classify the scenarios examined by the surveyed works with respect to the assumed parameters. Moreover, we systematize methods used to save energy in a fog network. These methods are compared with respect to their scenarios, objectives, constraints, and decision variables. Finally, we discuss future trends in fog networking and how related technologies and economics shall trade their increasing development with energy consumption. Full article
(This article belongs to the Special Issue Cloud-Edge Continuum in 5G Networks)
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