Convergence of IoT, Edge and Cloud Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 950

Special Issue Editors


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Guest Editor
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: edge intelligence; privacy-aware machine learning; cloud computing

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Guest Editor
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: system security; artificial intelligence security; network and information security
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Special Issue Information

Dear Colleagues,

The widespread adoption of smart devices and the Internet of Things (IoT) have a significant impact on the whole world. IoT devices generate massive amounts of data. Cloud computing provides the scalability, storage, and advanced analysis capabilities required for processing and analyzing large volumes of IoT data. But, processing these data in the cloud alone can lead to issues such as high latency, network congestion, and increased costs. Fortunately, edge computing aims to address these challenges by bringing computation and storage closer to the data source, reducing the need for data transmission to the cloud. Thus, this has led to the emergence of cloud-edge IoT, which integrates IoT with cloud computing and edge computing. The hierarchical cloud-edge IoT architecture offers numerous benefits by enabling data aggregation, storage, and processing. But, achieving an effective convergence of IoT, cloud, and edge computing requires overcoming various design, implementation, deployment, security, privacy, and evaluation challenges.

The goal of this Special Issue is to provide an overview of the latest developments regarding the convergence of IoT, edge and cloud systems. Both theoretical and technical aspects are of interest. Interdisciplinary approaches are also highly welcome.

Dr. Dandan Li
Dr. Li Duan
Guest Editors

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Keywords

  • cloud computing
  • edge intelligence
  • hybrid cloud-edge architectures
  • communication protocols
  • IoT device orchestration, industry application, security and privacy

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Published Papers (1 paper)

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Research

20 pages, 2522 KiB  
Article
Application of Fuzzy Logic for Horizontal Scaling in Kubernetes Environments within the Context of Edge Computing
by Sérgio N. Silva, Mateus A. S. de S. Goldbarg, Lucileide M. D. da Silva and Marcelo A. C. Fernandes
Future Internet 2024, 16(9), 316; https://doi.org/10.3390/fi16090316 - 2 Sep 2024
Viewed by 762
Abstract
This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, [...] Read more.
This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, the number of replicas used, and adherence to latency Service-Level Agreements (SLAs). The experiments were conducted in an environment simulating Edge Computing infrastructure, with virtual machines used to represent edge nodes and traffic generated via JMeter. The results demonstrate that FHS achieves a reduction in CPU consumption, uses fewer replicas under the same stress conditions, and exhibits more distributed SLA latency violation rates compared to HPA. These results indicate that FHS offers a more efficient and customizable solution for replica scaling in Kubernetes within Edge Computing environments, contributing to both operational efficiency and service quality. Full article
(This article belongs to the Special Issue Convergence of IoT, Edge and Cloud Systems)
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