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Edge and Fog Computing for Internet of Things Systems (2nd...
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Edge and Fog Computing for Internet of Things Systems (2nd Edition)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 978

Special Issue Editors

Dr. Luís Nogueira

E-Mail Website
Guest Editor
School of Engineering (ISEP), Polytechnic of Porto (IPP), 4249-015 Porto, Portugal
Interests: distributed systems; cloud computing; edge computing; IoT; real-time systems; quality of service; ad hoc networks
Special Issues, Collections and Topics in MDPI journals
Dr. Jorge Coelho

E-Mail Website
Guest Editor
1. School of Engineering (ISEP), Polytechnic of Porto (IPP), 4249-015 Porto, Portugal
2. Artificial Intelligence and Computer Science Laboratory, University of Porto (LIACC), 4099-002 Porto, Portugal
Interests: distributed systems; ad hoc networks; edge computing; IoT; network management and orchestration; computational offloading; applications of declarative programming languages
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Internet of Thing (IoT) devices have been vastly proliferated into many aspects of everyday life. The introduction of increasingly more capable devices makes them naturally central in the Edge and Fog computing paradigms. Exploring their interactions, collaboration, and communication capabilities is now a relevant research topic that enables the maximization of their use in several different scenarios.

Many problems, such as dealing with their heterogeneity, communication, computing, and storage capabilities, stem from the need to explore such devices in those contexts. Thus, new research is needed to find solutions for the various challenges resulting from the utilizations of devices such as Edge and Fog nodes. This Special Issue welcomes original research and review articles on all aspects of the use of IoT devices in the context of Edge and Fog computing paradigms. Topics of interest include, but are not limited to, the following areas:

  • IoT devices for Edge computing;
  • IoT devices for Fog computing;
  • Computational offloading;
  • Programming paradigms for IoT devices;
  • Protocols for distributed computing with IoT devices;
  • Ad-Hoc networks and IoT devices;
  • Abstraction of heterogeneous IoT devices;
  • Resource reservation in IoT devices;
  • Scheduling in IoT devices;
  • Parallel processing in IoT devices;
  • Coalitions of IoT devices;
  • Performance of IoT devices in the context of Edge and Fog;
  • QoS management in IoT devices;
  • Orchestration of IoT devices;
  • Security issues in IoT devices;
  • Embedded systems.

Dr. Luís Nogueira
Dr. Jorge Coelho
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.

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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • edge computing
  • fog computing
  • IoT
  • computational offloading
  • orchestration
  • distributed computing

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Published Papers (2 papers)

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18 pages, 565 KiB  
Article
Efficient Orchestration of Distributed Workloads in Multi-Region Kubernetes Cluster
by Radoslav Furnadzhiev, Mitko Shopov and Nikolay Kakanakov
Computers 2025, 14(4), 114; https://doi.org/10.3390/computers14040114 - 21 Mar 2025
Viewed by 318
Abstract
Distributed Kubernetes clusters provide robust solutions for geo-redundancy and fault tolerance in modern cloud architectures. However, default scheduling mechanisms primarily optimize for resource availability, often neglecting network topology, inter-node latency, and global resource efficiency, leading to suboptimal task placement in multi-region deployments. This [...] Read more.
Distributed Kubernetes clusters provide robust solutions for geo-redundancy and fault tolerance in modern cloud architectures. However, default scheduling mechanisms primarily optimize for resource availability, often neglecting network topology, inter-node latency, and global resource efficiency, leading to suboptimal task placement in multi-region deployments. This paper proposes network-aware scheduling plugins that integrate heuristic, metaheuristic, and linear programming methods to optimize resource utilization and inter-zone communication latency for containerized workloads, particularly Apache Spark batch-processing tasks. Unlike the default scheduler, the presented approach incorporates inter-node latency constraints and prioritizes locality-aware scheduling, ensuring efficient pod distribution while minimizing network overhead. The proposed plugins are evaluated using the kube-scheduler-simulator, a tool that replicates Kubernetes scheduling behavior without deploying real workloads. Experiments cover multiple cluster configurations, varying in node count, region count, and inter-region latencies, with performance metrics recorded for scheduler efficiency, inter-zone communication impact, and execution time across different optimization algorithms. The obtained results indicate that network-aware scheduling approaches significantly improve latency-aware placement decisions, achieving lower inter-region communication delays while maintaining resource efficiency. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems (2nd Edition))
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26 pages, 5848 KiB  
Article
A Novel, Self-Adaptive, Multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation
by Hicham Ben Alla, Said Ben Alla, Abdellah Ezzati and Abdellah Touhafi
Computers 2025, 14(3), 81; https://doi.org/10.3390/computers14030081 - 24 Feb 2025
Viewed by 299
Abstract
Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers [...] Read more.
Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities. Full article
(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems (2nd Edition))
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