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Electronics
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Next-Generation Cloud–Edge Computing: Systems and Applications
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Next-Generation Cloud–Edge Computing: Systems and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3531

Special Issue Editor

Dr. Ruhui Ma

E-Mail Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: cloud computing system; intelligent system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of emerging techniques in electronics—such as the Internet of Things (IoT), artificial intelligence (AI), 5G, and cloud computing—data generation and processing have undergone fundamental changes. The widespread deployment of IoT devices has led to exponential data growth across various scenarios, including smart homes, intelligent transportation, and industrial IoT. The traditional centralized cloud-computing model faces challenges such as high latency, limited network bandwidth, and data privacy and security issues when processing these data. Meanwhile, the low-latency and high-bandwidth features of 5G networks strongly support the development of edge computing. As a result, cloud–edge computing has emerged as the next-generation cloud computing paradigm.

However, existing edge-cloud computing systems still face challenges such as resource management across hierarchical nodes, maintaining data consistency between cloud and edge nodes, enhancing security and privacy protection mechanisms, and ensuring system reliability. New system-level designs are needed to address these challenges effectively. Meanwhile, cloud–edge computing has shown great potential in various fields, which calls for applications that fully utilize the ability of cloud–edge computing paradigm. The topics of interest for this Special Issue include, but are not limited to, the following:

  1. Resource management in heterogeneous cloud–edge systems.
  2. Task scheduling and load balancing in cloud–edge computing.
  3. Multi-edge node data synchronization.
  4. Privacy protection in cloud–edge architectures.
  5. Edge node security in cloud–edge computing.
  6. Fault-tolerant mechanisms in cloud–edge systems.
  7. Applications of cloud–edge computing in smart cities.
  8. Cloud–edge computing for intelligent transportation systems.
  9. Industrial IoT applications with cloud–edge computing.
  10. Healthcare applications of cloud–edge computing.

I look forward to receiving your contributions.

Dr. Ruhui Ma
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 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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • resource management
  • task scheduling
  • distributed computing
  • data synchronization
  • privacy protection
  • edge node security
  • fault-tolerant
  • energy management
  • IoT devices

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Published Papers (2 papers)

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Research

12 pages, 2071 KB  
Article
A Novel IEEE 1588 Synchronization Mechanism for Data Center Time Synchronization Based on the Original Path Return Method and Minimum Delay Packet Screening Algorithm
by Xinyu Miao, Changjun Hu and Yaojun Qiao
Electronics 2025, 14(22), 4375; https://doi.org/10.3390/electronics14224375 - 9 Nov 2025
Viewed by 738
Abstract
In order to solve the problem of the IEEE 1588 (precise time protocol, PTP) path delay asymmetry caused by network congestion in data center time synchronization, this paper proposes a novel PTP time synchronization mechanism based on the original path return method and [...] Read more.
In order to solve the problem of the IEEE 1588 (precise time protocol, PTP) path delay asymmetry caused by network congestion in data center time synchronization, this paper proposes a novel PTP time synchronization mechanism based on the original path return method and minimum delay packet screening (MDPS) algorithm. The original path return method utilizes the routing record and source station routing function of the IP protocol to enable the PTP packet to return along the original path, ensuring sufficient conditions for delay symmetry of the forth and back paths. The MDPS algorithm is proposed to select the packets on the same path whose delay is not affected by network congestion, thereby fundamentally eliminating the problem of delay asymmetry of forth and back paths in the case of network congestion. To verify the performance of the proposed mechanism, a simulation of the PTP packet queuing model and PTP time synchronization is conducted. The simulation results show that the uncongested packet can be obtained within 2.2 s. Moreover, the maximum absolute time deviation between the slave and master clocks is reduced by approximately 50 times, and the standard deviation of the time deviation is reduced by about 2 orders of magnitude. Full article
(This article belongs to the Special Issue Next-Generation Cloud–Edge Computing: Systems and Applications)
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36 pages, 14083 KB  
Article
Workload Prediction for Proactive Resource Allocation in Large-Scale Cloud-Edge Applications
by Thang Le Duc, Chanh Nguyen and Per-Olov Östberg
Electronics 2025, 14(16), 3333; https://doi.org/10.3390/electronics14163333 - 21 Aug 2025
Cited by 4 | Viewed by 2351
Abstract
Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive [...] Read more.
Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive feedback loop. The framework is evaluated using 18 months of real traffic traces from a production multi-tier CDN, capturing realistic workload seasonality, cache–tier interactions, and propagation delays. Unlike generic cloud-edge predictors, our design incorporates CDN-specific features and model-switching mechanisms to balance prediction accuracy with computational cost. Seasonal ARIMA (S-ARIMA), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Online Sequential Extreme Learning Machine (OS-ELM) are combined to support both short-horizon scaling and longer-term capacity planning. The predictions drive a queue-based resource-estimation model, enabling proactive cache–server scaling with low rejection rates. Experimental results demonstrate that the framework maintains high accuracy while reducing computational overhead through adaptive model selection. The proposed approach offers a practical, production-tested solution for predictive autoscaling in CDNs and can be extended to other latency-sensitive edge-cloud services with hierarchical architectures. Full article
(This article belongs to the Special Issue Next-Generation Cloud–Edge Computing: Systems and Applications)
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