2025
Maiko Andrade, Juliano Araujo Wickboldt
A Study on 5G Network Slice Isolation Based on Native Cloud and Edge Computing Tools Journal Article
In: Journal of Network and Systems Management (JNSM), 33 (90), 2025, ISSN: 1573-7705.
Abstract Links BibTeX Tags: 5G Cloud Computing Fog & Edge Computing Network Virtualization & Slicing
@article{journals/jnsm/Andrade5GSlice25,
title = {A Study on 5G Network Slice Isolation Based on Native Cloud and Edge Computing Tools},
author = {Maiko Andrade and Juliano Araujo Wickboldt},
url = {https://link.springer.com/article/10.1007/s10922-025-09958-5},
doi = {10.1007/s10922-025-09958-5},
issn = {1573-7705},
year = {2025},
date = {2025-07-09},
urldate = {2025-07-09},
journal = {Journal of Network and Systems Management (JNSM)},
volume = {33},
number = {90},
abstract = {5G networks support various advanced applications through network slicing, network function virtualization (NFV), and edge computing, ensuring low latency and service isolation. However, private 5G networks relying on open-source tools still face challenges in maturity and integration with edge/cloud platforms, compromising proper slice isolation. This study investigates resource allocation mechanisms to address this issue, conducting experiments in a hospital scenario with medical video conferencing. The results show that CPU limitations improve the performance of prioritized slices, while memory restrictions have minimal impact. The generated data and scripts have been made publicly available for future research and machine learning applications.},
keywords = {5G, Cloud Computing, Fog & Edge Computing, Network Virtualization & Slicing},
pubstate = {published},
tppubtype = {article}
}
5G networks support various advanced applications through network slicing, network function virtualization (NFV), and edge computing, ensuring low latency and service isolation. However, private 5G networks relying on open-source tools still face challenges in maturity and integration with edge/cloud platforms, compromising proper slice isolation. This study investigates resource allocation mechanisms to address this issue, conducting experiments in a hospital scenario with medical video conferencing. The results show that CPU limitations improve the performance of prioritized slices, while memory restrictions have minimal impact. The generated data and scripts have been made publicly available for future research and machine learning applications.
Felipe Machado Schwanck, Marcos Tomazzoli Leipnitz, Joel Luís Carbonera, Juliano Araujo Wickboldt
A Framework for testing Federated Learning algorithms using an edge-like environment Journal Article
In: Future Generation Computer Systems, 166 , pp. 107626, 2025, ISSN: 0167-739X.
Abstract Links BibTeX Tags: Cloud Computing Federated Learning Fog & Edge Computing Machine Learning
@article{journal/fgcs/Schwanck25,
title = {A Framework for testing Federated Learning algorithms using an edge-like environment},
author = {Felipe Machado Schwanck and Marcos Tomazzoli Leipnitz and Joel Luís Carbonera and Juliano Araujo Wickboldt},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24005909},
doi = {10.1016/j.future.2024.107626},
issn = {0167-739X},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
journal = {Future Generation Computer Systems},
volume = {166},
pages = {107626},
abstract = {Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where data are created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and evaluating FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to evaluate FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).},
keywords = {Cloud Computing, Federated Learning, Fog & Edge Computing, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where data are created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and evaluating FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to evaluate FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).
Marcos Tomazzoli Leipnitz, Rafael Humann Petry, Fabrício Henrique Rodrigues, Haroldo Rojas Souza Silva, Jaqueline Bitencourt Correia, Karin Becker, Juliano Araujo Wickboldt, Joel Luis Carbonera, João Cesar Netto, Mara Abel
Architecting Digital Twins: From Concept to Reality Journal Article
In: Procedia Computer Science, 256 , pp. 530-537, 2025, ISSN: 1877-0509, (Proceedings: International Conference on ENTERprise Information Systems (CENTERIS)).
Abstract Links BibTeX Tags: Cloud Computing Digital Twin Fog & Edge Computing
@article{conf/centeris/LeipnitzPetwin24,
title = {Architecting Digital Twins: From Concept to Reality},
author = {Marcos Tomazzoli Leipnitz and Rafael Humann Petry and Fabrício Henrique Rodrigues and Haroldo Rojas Souza Silva and Jaqueline Bitencourt Correia and Karin Becker and Juliano Araujo Wickboldt and Joel Luis Carbonera and João Cesar Netto and Mara Abel},
url = {https://www.sciencedirect.com/science/article/pii/S187705092500506X},
doi = {10.1016/j.procs.2025.02.149},
issn = {1877-0509},
year = {2025},
date = {2025-03-14},
urldate = {2025-03-14},
journal = {Procedia Computer Science},
volume = {256},
pages = {530-537},
publisher = {Elsevier},
abstract = {Digital twins (DTs) have emerged as a prominent technology across various industries. However, their development in the Oil and Gas sector faces challenges related to the size and complexity of the domain, which leads to a focus on specific sub-problems like maintenance or optimization with hard, further integrated solutions. These DTs typically lack open architectures since they base the implementations on third-party solutions. This lack of architecture frameworks hinders rapid and scalable DT development and complicates the integration of new assets, services, and data sources. This paper introduces a semantic-oriented DT Framework from the PeTWIN project, which aims to address these challenges by offering (i) a data-centric, data-driven software solution, (ii) an open, modular, and extensible microservices architecture, and (iii) a semantic ontology-based strategy for integration and interoperability. The paper details the key architectural components, behaviours, and interfaces to meet industry needs and provides a proof of concept demonstrating the framework’s capabilities.},
note = {Proceedings: International Conference on ENTERprise Information Systems (CENTERIS)},
keywords = {Cloud Computing, Digital Twin, Fog & Edge Computing},
pubstate = {published},
tppubtype = {article}
}
Digital twins (DTs) have emerged as a prominent technology across various industries. However, their development in the Oil and Gas sector faces challenges related to the size and complexity of the domain, which leads to a focus on specific sub-problems like maintenance or optimization with hard, further integrated solutions. These DTs typically lack open architectures since they base the implementations on third-party solutions. This lack of architecture frameworks hinders rapid and scalable DT development and complicates the integration of new assets, services, and data sources. This paper introduces a semantic-oriented DT Framework from the PeTWIN project, which aims to address these challenges by offering (i) a data-centric, data-driven software solution, (ii) an open, modular, and extensible microservices architecture, and (iii) a semantic ontology-based strategy for integration and interoperability. The paper details the key architectural components, behaviours, and interfaces to meet industry needs and provides a proof of concept demonstrating the framework’s capabilities.
Alexandre Gustavo Wermann, Juliano Araujo Wickboldt
KTWIN: A serverless Kubernetes-based Digital Twin platform Journal Article
In: Computer Networks, 259 , pp. 111095, 2025, ISSN: 1389-1286.
Abstract Links BibTeX Tags: Cloud Computing Digital Twin Fog & Edge Computing
@article{journal/comnet/Wermann25,
title = {KTWIN: A serverless Kubernetes-based Digital Twin platform},
author = {Alexandre Gustavo Wermann and Juliano Araujo Wickboldt},
url = {https://www.sciencedirect.com/science/article/pii/S1389128625000635},
doi = {10.1016/j.comnet.2025.111095},
issn = {1389-1286},
year = {2025},
date = {2025-02-12},
journal = {Computer Networks},
volume = {259},
pages = {111095},
abstract = {Digital Twins (DTs) systems are virtual representations of physical assets allowing organizations to gain insights and improve existing processes. In practice, DTs require proper modeling, coherent development and seamless deployment along cloud and edge landscapes relying on established patterns to reduce operational costs. In this work, we propose KTWIN a Kubernetes-based Serverless Platform for Digital Twins. KTWIN was developed using the state-of-the-art open-source Cloud Native tools, allowing DT operators to easily define models through open standards and configure details of the underlying services and infrastructure. The experiments carried out with the developed prototype show that KTWIN can provide a higher level of abstraction to model and deploy a Digital Twin use case without compromising the solution scalability. The tests performed also show cost savings ranging between 60% and 80% compared to overprovisioned scenarios.},
keywords = {Cloud Computing, Digital Twin, Fog & Edge Computing},
pubstate = {published},
tppubtype = {article}
}
Digital Twins (DTs) systems are virtual representations of physical assets allowing organizations to gain insights and improve existing processes. In practice, DTs require proper modeling, coherent development and seamless deployment along cloud and edge landscapes relying on established patterns to reduce operational costs. In this work, we propose KTWIN a Kubernetes-based Serverless Platform for Digital Twins. KTWIN was developed using the state-of-the-art open-source Cloud Native tools, allowing DT operators to easily define models through open standards and configure details of the underlying services and infrastructure. The experiments carried out with the developed prototype show that KTWIN can provide a higher level of abstraction to model and deploy a Digital Twin use case without compromising the solution scalability. The tests performed also show cost savings ranging between 60% and 80% compared to overprovisioned scenarios.
2023
Francisco Paiva Knebel, Rafael Trevisan, Givanildo Santana Nascimento, Mara Abel, Juliano Araujo Wickboldt
A study on cloud and edge computing for the implementation of digital twins in the Oil & Gas industries Journal Article
In: Computers & Industrial Engineering, 182 , pp. 109363, 2023, ISSN: 0360-8352.
Abstract Links BibTeX Tags: Cloud Computing Digital Twin Fog & Edge Computing
@article{journal/caie/KnebelGuidelinesOG23,
title = {A study on cloud and edge computing for the implementation of digital twins in the Oil & Gas industries},
author = {Francisco Paiva Knebel and Rafael Trevisan and Givanildo Santana Nascimento and Mara Abel and Juliano Araujo Wickboldt},
url = {https://www.sciencedirect.com/science/article/pii/S036083522300387X},
doi = {10.1016/j.cie.2023.109363},
issn = {0360-8352},
year = {2023},
date = {2023-06-15},
urldate = {2023-06-15},
journal = {Computers & Industrial Engineering},
volume = {182},
pages = {109363},
abstract = {In the Oil and Gas industry, minor accidents can negatively affect people, the environment, and the enterprise on a grand scale. For this type of business, it is crucial to have adequate monitoring and maintenance operational routines. The concept of digital twins has been widely discussed as an industrial solution capable of monitoring objects in real-time, predicting their state, integrity, and safety conditions, and providing user feedback. This analysis can also perform predictive maintenance and estimate efficiency levels. This paper follows a systematic approach and inspects the current state of digital twin adoption by the Oil and Gas industry, along with cloud and edge utilization in both scenarios. The survey was conducted by filtering and selecting articles published in three different queries regarding digital twins in the Oil and Gas industry, cloud and edge usage in digital twins, and cloud and edge usage in the Oil and Gas industry. The selected articles were classified and reviewed to present an overview of the current literature regarding digital twins in the oil and gas industry and evaluate cloud-based solutions for digital twins in this context. This review found that cloud and edge adoption in the Oil and Gas industry was late compared to other industries, mainly because of security and data privacy concerns. Digital twin adoption across all industries is still in its infancy, as most of the current works are theoretical or present partial implementations, which often follow different digital twin definitions. Cloud and edge computing grant access to increased storage and computational capabilities and decouple the necessity of relying on a robust local IT infrastructure, being critical enabling factors for digital twin implementation.},
keywords = {Cloud Computing, Digital Twin, Fog & Edge Computing},
pubstate = {published},
tppubtype = {article}
}
In the Oil and Gas industry, minor accidents can negatively affect people, the environment, and the enterprise on a grand scale. For this type of business, it is crucial to have adequate monitoring and maintenance operational routines. The concept of digital twins has been widely discussed as an industrial solution capable of monitoring objects in real-time, predicting their state, integrity, and safety conditions, and providing user feedback. This analysis can also perform predictive maintenance and estimate efficiency levels. This paper follows a systematic approach and inspects the current state of digital twin adoption by the Oil and Gas industry, along with cloud and edge utilization in both scenarios. The survey was conducted by filtering and selecting articles published in three different queries regarding digital twins in the Oil and Gas industry, cloud and edge usage in digital twins, and cloud and edge usage in the Oil and Gas industry. The selected articles were classified and reviewed to present an overview of the current literature regarding digital twins in the oil and gas industry and evaluate cloud-based solutions for digital twins in this context. This review found that cloud and edge adoption in the Oil and Gas industry was late compared to other industries, mainly because of security and data privacy concerns. Digital twin adoption across all industries is still in its infancy, as most of the current works are theoretical or present partial implementations, which often follow different digital twin definitions. Cloud and edge computing grant access to increased storage and computational capabilities and decouple the necessity of relying on a robust local IT infrastructure, being critical enabling factors for digital twin implementation.
2022
Fernando Ferreira Remde, Juliano Araujo Wickboldt
Analyzing Federated Learning Performance in Distributed Edge Scenarios Inproceedings
In: 27º Workshop de Gerência e Operação de Redes e Serviços, WGRS 2022, Virtual Conference, May 23-27, 2022, pp. 155-168, SBC, 2022, ISSN: 2595-2722.
Abstract Links BibTeX Tags: Federated Learning Fog & Edge Computing Machine Learning
@inproceedings{conf/wgrs/Remde22,
title = {Analyzing Federated Learning Performance in Distributed Edge Scenarios},
author = {Fernando Ferreira Remde and Juliano Araujo Wickboldt},
url = {https://www.inf.ufrgs.br/~jwickboldt/wp-content/uploads/Analyzing-Federated-Learning-Performance-in-Distributed-Edge-Scenarios-WGRS-2022-Camera-Ready.pdf
https://sol.sbc.org.br/index.php/wgrs/article/view/21484},
doi = {10.5753/wgrs.2022.223574},
issn = {2595-2722},
year = {2022},
date = {2022-05-23},
urldate = {2022-05-23},
booktitle = {27º Workshop de Gerência e Operação de Redes e Serviços, WGRS 2022, Virtual Conference, May 23-27, 2022},
pages = {155-168},
publisher = {SBC},
abstract = {Federated Learning is a machine learning paradigm where many clients cooperatively train a single centralized model while keeping their data private and decentralized. This novel paradigm imposes many challenges, such as dealing with data that is not independent and identically distributed, spread among multiple clients that are not synchronized and may have limited computing power. These clients are often edge devices such as smartphones and sensors, which form a system that is heterogeneous, highly distributed by nature and difficult to manage. This work proposes an architecture for running federated learning experiments in a distributed edge-like environment. Based on this architecture, a set of experiments are conducted to analyze how the overall system performance is affected by different configuration parameters and varied number of connected clients.},
keywords = {Federated Learning, Fog & Edge Computing, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Federated Learning is a machine learning paradigm where many clients cooperatively train a single centralized model while keeping their data private and decentralized. This novel paradigm imposes many challenges, such as dealing with data that is not independent and identically distributed, spread among multiple clients that are not synchronized and may have limited computing power. These clients are often edge devices such as smartphones and sensors, which form a system that is heterogeneous, highly distributed by nature and difficult to manage. This work proposes an architecture for running federated learning experiments in a distributed edge-like environment. Based on this architecture, a set of experiments are conducted to analyze how the overall system performance is affected by different configuration parameters and varied number of connected clients.
2021
Francisco Paiva Knebel, Juliano Araujo Wickboldt, Edison Pignaton de Freitas
A Cloud-Fog Computing Architecture for Real-Time Digital Twins Unpublished
arXiv Preprint, 2021, (Computer Science > Networking and Internet Architecture (cs.NI)).
Abstract Links BibTeX Tags: Cloud Computing Digital Twin Fog & Edge Computing
@unpublished{knebel2020cloud,
title = {A Cloud-Fog Computing Architecture for Real-Time Digital Twins},
author = {Francisco Paiva Knebel and Juliano Araujo Wickboldt and Edison Pignaton de Freitas},
url = {https://arxiv.org/abs/2012.06118},
year = {2021},
date = {2021-05-07},
urldate = {2021-05-07},
abstract = {Digital Twin systems are designed as two interconnected mirrored spaces, one real and one virtual, each reflecting the other, sharing information, and making predictions based on analysis and simulations. The correct behavior of a real-time Digital Twin depends not only on the logical results of computation but also on the timing constraints. To cope with the large amounts of data that need to be stored and analyzed, modern large scale Digital Twin deployments often rely on cloud-based architectures. A significant portion of the overall response time of a Digital Twin is spent on moving data from the edge to the cloud. Therefore, implementing Digital Twins using cloud-fog architectures emerges as an alternative to bring computing power closer to the edge, reducing latency and allowing faster response times. This paper studies how suitable the use of a cloud-fog architecture is to handle the real-time requirements of Digital Twins. Based on a realistic implementation and deployment of Digital Twin software components, it is possible to conclude that the distribution of Digital Twins in a fog computing setup can reduce response times, meeting its real-time application requirements.},
howpublished = {arXiv Preprint},
note = {Computer Science > Networking and Internet Architecture (cs.NI)},
keywords = {Cloud Computing, Digital Twin, Fog & Edge Computing},
pubstate = {published},
tppubtype = {unpublished}
}
Digital Twin systems are designed as two interconnected mirrored spaces, one real and one virtual, each reflecting the other, sharing information, and making predictions based on analysis and simulations. The correct behavior of a real-time Digital Twin depends not only on the logical results of computation but also on the timing constraints. To cope with the large amounts of data that need to be stored and analyzed, modern large scale Digital Twin deployments often rely on cloud-based architectures. A significant portion of the overall response time of a Digital Twin is spent on moving data from the edge to the cloud. Therefore, implementing Digital Twins using cloud-fog architectures emerges as an alternative to bring computing power closer to the edge, reducing latency and allowing faster response times. This paper studies how suitable the use of a cloud-fog architecture is to handle the real-time requirements of Digital Twins. Based on a realistic implementation and deployment of Digital Twin software components, it is possible to conclude that the distribution of Digital Twins in a fog computing setup can reduce response times, meeting its real-time application requirements.
2020
Henrique Cesar Carvalho de Resende, Matias Artur Klafke Schimuneck, Cristiano Bonato Both, Juliano Araujo Wickboldt, Johann M. Márquez-Barja
COPA: Experimenter-level Container Orchestration for Networking Testbeds Journal Article
In: IEEE Access, 8 , pp. 201781-201798, 2020, ISSN: 2169-3536.
Abstract Links BibTeX Tags: 5G Cloud Computing Container Management & Orchestration Fog & Edge Computing Network Orchestration Network Virtualization & Slicing
@article{journal/access/Resende20,
title = {COPA: Experimenter-level Container Orchestration for Networking Testbeds},
author = {Henrique Cesar Carvalho de Resende and Matias Artur Klafke Schimuneck and Cristiano Bonato Both and Juliano Araujo Wickboldt and Johann M. Márquez-Barja},
url = {https://ieeexplore.ieee.org/document/9247110},
doi = {10.1109/ACCESS.2020.3035619},
issn = {2169-3536},
year = {2020},
date = {2020-11-03},
urldate = {2020-11-03},
journal = {IEEE Access},
volume = {8},
pages = {201781-201798},
abstract = {As Cloud Computing (CC) branched areas such as Multi-access Edge Computing (MEC) and Fog computing are still on growing research interest. The creation of new tools to improve quality and speed the experimentation in such areas is a general interest. In this article, we propose COPA, an experimenter-level container orchestration tool for networking testbeds. This tool provides a friendly interface for the experimenter test container orchestration algorithms which can start, stop, copy, and even migrate a container from one host to another. COPA also includes network/resources monitoring to feed the experimenter’s orchestration algorithm so that it can make decisions based on real-time environment information. Furthermore, the experimenter can automatize the experiment scenario setup and deployment by pre-configuring in COPA. This tool helps the experimenter in testing different scenarios and quickly changing experiment parameters. Considering these features, COPA aims to provide an experimentation architecture to deploy and test container orchestration algorithms. Furthermore, we provide a case study explaining how COPA can be a key tool in the MEC and Network Function Virtualization (NFV) experimentation environments. This tool was already deployed in Federated Union of Telecommunications Research Facilities for an EU-Brazil Open Laboratory (FUTEBOL) testbeds as part of the control framework and was well validated by the project reviewers and partners.},
keywords = {5G, Cloud Computing, Container Management & Orchestration, Fog & Edge Computing, Network Orchestration, Network Virtualization & Slicing},
pubstate = {published},
tppubtype = {article}
}
As Cloud Computing (CC) branched areas such as Multi-access Edge Computing (MEC) and Fog computing are still on growing research interest. The creation of new tools to improve quality and speed the experimentation in such areas is a general interest. In this article, we propose COPA, an experimenter-level container orchestration tool for networking testbeds. This tool provides a friendly interface for the experimenter test container orchestration algorithms which can start, stop, copy, and even migrate a container from one host to another. COPA also includes network/resources monitoring to feed the experimenter’s orchestration algorithm so that it can make decisions based on real-time environment information. Furthermore, the experimenter can automatize the experiment scenario setup and deployment by pre-configuring in COPA. This tool helps the experimenter in testing different scenarios and quickly changing experiment parameters. Considering these features, COPA aims to provide an experimentation architecture to deploy and test container orchestration algorithms. Furthermore, we provide a case study explaining how COPA can be a key tool in the MEC and Network Function Virtualization (NFV) experimentation environments. This tool was already deployed in Federated Union of Telecommunications Research Facilities for an EU-Brazil Open Laboratory (FUTEBOL) testbeds as part of the control framework and was well validated by the project reviewers and partners.