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Search Results (178)

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Keywords = cloud–native

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15 pages, 2861 KB  
Article
Sustainable Real-Time NLP with Serverless Parallel Processing on AWS
by Chaitanya Kumar Mankala and Ricardo J. Silva
Information 2025, 16(10), 903; https://doi.org/10.3390/info16100903 (registering DOI) - 15 Oct 2025
Abstract
This paper proposes a scalable serverless architecture for real-time natural language processing (NLP) on large datasets using Amazon Web Services (AWS). The framework integrates AWS Lambda, Step Functions, and S3 to enable fully parallel sentiment analysis with Transformer-based models such as DistilBERT, RoBERTa, [...] Read more.
This paper proposes a scalable serverless architecture for real-time natural language processing (NLP) on large datasets using Amazon Web Services (AWS). The framework integrates AWS Lambda, Step Functions, and S3 to enable fully parallel sentiment analysis with Transformer-based models such as DistilBERT, RoBERTa, and ClinicalBERT. By containerizing inference workloads and orchestrating parallel execution, the system eliminates the need for dedicated servers while dynamically scaling to workload demand. Experimental evaluation on the IMDb Reviews dataset demonstrates substantial efficiency gains: parallel execution achieved a 6.07× reduction in wall-clock duration, an 81.2% reduction in total computing time and energy consumption, and a 79.1% reduction in variable costs compared to sequential processing. These improvements directly translate into a smaller carbon footprint, highlighting the sustainability benefits of serverless architectures for AI workloads. The findings show that the proposed framework is model-independent and provides consistent advantages across diverse Transformer variants. This work illustrates how cloud-native, event-driven infrastructures can democratize access to large-scale NLP by reducing cost, processing time, and environmental impact while offering a reproducible pathway for real-world research and industrial applications. Full article
(This article belongs to the Special Issue Generative AI Transformations in Industrial and Societal Applications)
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19 pages, 12926 KB  
Article
Mapping Banana and Peach Palm in Diversified Landscapes in the Brazilian Atlantic Forest with Sentinel-2
by Victória Beatriz Soares, Taya Cristo Parreiras, Danielle Elis Garcia Furuya, Édson Luis Bolfe and Katia de Lima Nechet
Agriculture 2025, 15(19), 2052; https://doi.org/10.3390/agriculture15192052 - 30 Sep 2025
Viewed by 414
Abstract
Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the [...] Read more.
Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas (Musa spp.) and peach palms (Bactris gasipaes), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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31 pages, 792 KB  
Review
An Overview on the Landscape of Self-Adaptive Cloud Design and Operation Patterns: Goals, Strategies, Tooling, Evaluation, and Dataset Perspectives
by Apostolos Angelis and George Kousiouris
Future Internet 2025, 17(10), 434; https://doi.org/10.3390/fi17100434 - 24 Sep 2025
Viewed by 460
Abstract
Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This work systematically reviews 111 publications from the [...] Read more.
Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This work systematically reviews 111 publications from the last eight years on self-adaptive cloud design and operations patterns, classifying them by objectives, control scope, decision-making approach, automation level, and validation methods. Our analysis reveals that performance optimization dominates research goals, followed by cost reduction and security enhancement, with availability and reliability underexplored. Reactive feedback loops prevail, while proactive approaches—often leveraging machine learning—are increasingly applied to predictive resource provisioning and application management. Resource-oriented adaptation strategies are common, but direct application-level reconfiguration remains scarce, representing a promising research gap. We further catalog tools, platforms, and more than 30 publicly accessible datasets used in validation, and that dataset usage is fragmented without a de facto standard. Finally, we map the research findings on a generic application and system-level design for self-adaptive applications, including a proposal for a federated learning approach for SaaS application Agents. This blueprint aims to guide future work toward more intelligent, context-aware cloud automation. Full article
(This article belongs to the Section Internet of Things)
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18 pages, 2920 KB  
Article
UniTwin: Enabling Multi-Digital Twin Coordination for Modeling Distributed and Complex Systems
by Tim Markus Häußermann, Joel Lehmann, Florian Kolb, Alessa Rache and Julian Reichwald
IoT 2025, 6(4), 57; https://doi.org/10.3390/iot6040057 - 23 Sep 2025
Viewed by 358
Abstract
The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. [...] Read more.
The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. This extension enables the hierarchical aggregation of DTs across various abstraction levels. Traditional DT frameworks often lack mechanisms for dynamic composition at the level of entire systems. This is essential for modeling distributed systems in heterogeneous environments. UniTwin addresses this gap by grouping DTs into composite entities with an aggregation mechanism. The aggregation mechanism is demonstrated in a smart manufacturing case study, which covers the orchestration of a production line for personalized shopping cart chips. It uses modular DTs provided for each device within the production line. A System-Aggregated Digital Twin (S-ADT) is used to orchestrate the individual DTs, mapping the devices in the production line. Therefore, the production line adapts and reconfigures according to user-defined parameters. This validates the flexibility and practicality of the aggregation mechanism. This work contributes an aggregation mechanism for the UniTwin framework, paving the way for adaptable DTs for complex CPSs in domains like smart manufacturing, logistics, and infrastructure. Full article
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23 pages, 881 KB  
Article
From Digital Services to Sustainable Ones: Novel Industry 5.0 Environments Enhanced by Observability
by Andrea Sabbioni, Antonio Corradi, Stefano Monti and Carlos Roberto De Rolt
Information 2025, 16(9), 821; https://doi.org/10.3390/info16090821 - 22 Sep 2025
Viewed by 421
Abstract
The rapid evolution of Information Technologies is deeply transforming manufacturing, demanding innovative and enhanced production paradigms. Industry 5.0 further advances that transformation by fostering a more resilient, sustainable, and human-centric industrial ecosystem, built on the seamless integration of all value chains. This shift [...] Read more.
The rapid evolution of Information Technologies is deeply transforming manufacturing, demanding innovative and enhanced production paradigms. Industry 5.0 further advances that transformation by fostering a more resilient, sustainable, and human-centric industrial ecosystem, built on the seamless integration of all value chains. This shift requires the timely collection and intelligent analysis of vast, heterogeneous data sources, including IoT devices, digital services, crowdsourcing platforms, and last but not least important human input, which is essential to drive innovation. With sustainability as a key priority, pervasive monitoring not only enables optimization to reduce greenhouse gas emissions but also plays a strategic role across the manufacturing economy. This work introduces Observability platform for Industry 5.0 (ObsI5), a novel observability framework specifically designed to support Industry 5.0 environments. ObsI5 extends cloud-native observability tools, originally developed for IT service monitoring, into manufacturing infrastructures, enabling the collection, analysis, and control of data across both IT and OT domains. Our solution integrates human contributions as active data sources and leverages standard observability practices to extract actionable insights from all available resources. We validate ObsI5 through a dedicated test bed, demonstrating its ability to meet the strict requirements of Industry 5.0 in terms of timeliness, security, and modularity. Full article
(This article belongs to the Section Information Processes)
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23 pages, 1262 KB  
Article
Confidential Kubernetes Deployment Models: Architecture, Security, and Performance Trade-Offs
by Eduardo Falcão, Fernando Silva, Carlos Pamplona, Anderson Melo, A S M Asadujjaman and Andrey Brito
Appl. Sci. 2025, 15(18), 10160; https://doi.org/10.3390/app151810160 - 17 Sep 2025
Viewed by 999
Abstract
Cloud computing brings numerous advantages that can be leveraged through containerized workloads to deliver agile, dependable, and cost-effective microservices. However, the security of such cloud-based services depends on the assumption of trusting potentially vulnerable components, such as code installed on the host. The [...] Read more.
Cloud computing brings numerous advantages that can be leveraged through containerized workloads to deliver agile, dependable, and cost-effective microservices. However, the security of such cloud-based services depends on the assumption of trusting potentially vulnerable components, such as code installed on the host. The addition of confidential computing technology to the cloud computing landscape brings the possibility of stronger security guarantees by removing such assumptions. Nevertheless, the merger of containerization and confidential computing technologies creates a complex ecosystem. In this work, we show how Kubernetes workloads can be secured despite these challenges. In addition, we design, analyze, and evaluate five different Kubernetes deployment models using the infrastructure of three of the most popular cloud providers with CPUs from two major vendors. Our evaluation shows that performance can vary significantly across the possible deployment models while remaining similar across CPU vendors and cloud providers. Our security analysis highlights the trade-offs between different workload isolation levels, trusted computing base size, and measurement reproducibility. Through a comprehensive performance, security, and financial analysis, we identify the deployment models best suited to different scenarios. Full article
(This article belongs to the Special Issue Secure Cloud Computing Infrastructures)
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13 pages, 1716 KB  
Article
Hybrid Approach to Enabling Cross-Domain Service Orchestration over Heterogeneous Infrastructures
by Jane Frances Pajo, Geir Egeland, Sarang Kahvazadeh, Hamzeh Khalili, Martin Tolan, Ryan McCloskey, Min Xie and Olai Bendik Erdal
Sensors 2025, 25(18), 5804; https://doi.org/10.3390/s25185804 - 17 Sep 2025
Viewed by 452
Abstract
Efforts to lower the barriers for 5G uptake have brought forth accelerated innovation rates in a wide range of verticals. Consequently, the demands for 5G experimentation facilities have recently emerged from small- and medium-sized enterprises (SMEs) and 3rd party developers, requiring the abstraction [...] Read more.
Efforts to lower the barriers for 5G uptake have brought forth accelerated innovation rates in a wide range of verticals. Consequently, the demands for 5G experimentation facilities have recently emerged from small- and medium-sized enterprises (SMEs) and 3rd party developers, requiring the abstraction of the complexities of the underlying infrastructure, platform, and related management and orchestration (MANO) systems, especially in multi-domain scenarios. This paper proposes a novel approach towards cross-domain service orchestration, which combines the flexibility of supporting different 5G Service Orchestrators (SOs) in various domains, while preserving compatibility through NetApps. The Cross-domain Service Orchestrator (CDSO) is based on ETSI’s Open Source MANO (OSM), with a Requests Handler module on top, which acts as the main integration point to the different domains’ 5G SOs and other custom systems that are northbound. This would facilitate the interworking among independently orchestrated domains in supporting multi-domain services. The EU Horizon 2020 project, 5GMediaHUB, is presented as a use case, together with a first implementation of the Requests Handler and corresponding integrations. Nonetheless, the proposed approach is vertical-agnostic and is foreseen to accelerate service innovation and digitalization in any industry by laying the foundations in terms of service management and interconnectivity. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2025)
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19 pages, 611 KB  
Article
Prompt-Driven and Kubernetes Error Report-Aware Container Orchestration
by Niklas Beuter, André Drews and Nane Kratzke
Future Internet 2025, 17(9), 416; https://doi.org/10.3390/fi17090416 - 11 Sep 2025
Viewed by 387
Abstract
Background: Container orchestration systems like Kubernetes rely heavily on declarative manifest files, which serve as orchestration blueprints. However, managing these manifest files is often complex and requires substantial DevOps expertise. Methodology: This study investigates the use of Large Language Models (LLMs) to automate [...] Read more.
Background: Container orchestration systems like Kubernetes rely heavily on declarative manifest files, which serve as orchestration blueprints. However, managing these manifest files is often complex and requires substantial DevOps expertise. Methodology: This study investigates the use of Large Language Models (LLMs) to automate the creation of Kubernetes manifest files from natural language specifications, utilizing prompt engineering techniques within an innovative error- and warning-report–aware refinement process. We assess the capabilities of these LLMs using Zero-Shot, Few-Shot, Prompt-Chaining, and Self-Refine methods to address DevOps needs and support fully automated deployment pipelines. Results: Our findings show that LLMs can generate Kubernetes manifests with varying levels of manual intervention. Notably, GPT-4 and GPT-3.5 demonstrate strong potential for deployment automation. Interestingly, smaller models sometimes outperform larger ones, challenging the assumption that larger models always yield better results. Conclusions: This research highlights the crucial impact of prompt engineering on LLM performance for Kubernetes tasks and recommends further exploration of prompt techniques and model comparisons, outlining a promising path for integrating LLMs into automated deployment workflows. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Natural Language Processing (NLP))
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17 pages, 2598 KB  
Article
Evaluating the Performance Impact of Data Sovereignty Features on Data Spaces
by Stanisław Galij, Grzegorz Pawlak and Sławomir Grzyb
Appl. Sci. 2025, 15(17), 9841; https://doi.org/10.3390/app15179841 - 8 Sep 2025
Viewed by 592
Abstract
Data Spaces appear to offer a solution to data sovereignty concerns in public cloud environments, which are managed by third parties and must therefore be considered potentially untrusted. The IDS Connector, a key component of Data Space architecture, acts as a secure gateway, [...] Read more.
Data Spaces appear to offer a solution to data sovereignty concerns in public cloud environments, which are managed by third parties and must therefore be considered potentially untrusted. The IDS Connector, a key component of Data Space architecture, acts as a secure gateway, enforcing data sovereignty by controlling data usage and ensuring that data processing occurs within a trusted and verifiable environment. This study compares the performance of cloud-native data sharing services offered by major cloud providers—Amazon, Microsoft, and Google—with Data Spaces services delivered via two connector implementations: the Dataspace Connector and the Prometheus-X Dataspace Connector. An extensive set of experiments reveals significant differences in the performance of cloud-native managed services, as well as between connector implementations and hosting methods. The results indicate that the differences in the performance of data sharing services are unexpectedly substantial between providers, reaching up to 187%, and that the performance of different connector implementations also varies considerably, with an average difference of 56%. This indicates that the choice of cloud provider and data space Connector implementation has a major impact on the performance of the designed solution. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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20 pages, 1919 KB  
Article
Management of Virtualized Railway Applications
by Ivaylo Atanasov, Evelina Pencheva and Kamelia Nikolova
Information 2025, 16(8), 712; https://doi.org/10.3390/info16080712 - 21 Aug 2025
Viewed by 465
Abstract
Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with [...] Read more.
Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with the legacy communication system (GSM-R), it provides high data rates, ultra-high reliability, and low latency. The FRMCS architecture will also benefit from cloud computing, following the principles of the cloud-native 5G core network design based on Network Function Virtualization (NFV). In this paper, an approach to the management of virtualized FRMCS applications is presented. First, the key management functionality related to the virtualized FRMCS application is identified based on an analysis of the different use cases. Next, this functionality is synthesized as RESTful services. The communication between application management and the services is designed as Application Programing Interfaces (APIs). The APIs are formally verified by modeling the management states of an FRMCS application instance from different points of view, and it is mathematically proved that the management state models are synchronized in time. The latency introduced by the designed APIs, as a key performance indicator, is evaluated through emulation. Full article
(This article belongs to the Section Information Applications)
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10 pages, 724 KB  
Article
Real-Time Speech-to-Text on Edge: A Prototype System for Ultra-Low Latency Communication with AI-Powered NLP
by Stefano Di Leo, Luca De Cicco and Saverio Mascolo
Information 2025, 16(8), 685; https://doi.org/10.3390/info16080685 - 11 Aug 2025
Viewed by 3232
Abstract
This paper presents a real-time speech-to-text (STT) system designed for edge computing environments requiring ultra-low latency and local processing. Differently from cloud-based STT services, the proposed solution runs entirely on a local infrastructure which allows the enforcement of user privacy and provides high [...] Read more.
This paper presents a real-time speech-to-text (STT) system designed for edge computing environments requiring ultra-low latency and local processing. Differently from cloud-based STT services, the proposed solution runs entirely on a local infrastructure which allows the enforcement of user privacy and provides high performance in bandwidth-limited or offline scenarios. The designed system is based on a browser-native audio capture through WebRTC, real-time streaming with WebSocket, and offline automatic speech recognition (ASR) utilizing the Vosk engine. A natural language processing (NLP) component, implemented as a microservice, improves transcription results for spelling accuracy and clarity. Our prototype reaches sub-second end-to-end latency and strong transcription capabilities under realistic conditions. Furthermore, the modular architecture allows extensibility, integration of advanced AI models, and domain-specific adaptations. Full article
(This article belongs to the Section Information Applications)
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41 pages, 2180 KB  
Systematic Review
On the Application of Artificial Intelligence and Cloud-Native Computing to Clinical Research Information Systems: A Systematic Literature Review
by Isabel Bejerano-Blázquez and Miguel Familiar-Cabero
Information 2025, 16(8), 684; https://doi.org/10.3390/info16080684 - 10 Aug 2025
Viewed by 1997
Abstract
The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on [...] Read more.
The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on personalized medicine. Nevertheless, it also faces significant challenges due to rising costs, increased complexity, and regulatory hurdles. Through a systematic literature review (SLR) as a research method combined with a comprehensive market analysis, this paper explores how several leading early-adopter healthcare companies are increasing their investments in computer-based clinical research information systems (CRISs) to sustain productivity, particularly through the adoption of artificial intelligence (AI) and cloud-native computing. As an extension of this research, a novel 360-degree reference blueprint is proposed for the domain analysis of medical features within AI-powered CRIS applications. This theoretical framework specifically targets clinical trial management systems (CRIS-CTMSs). Additionally, a detailed review is presented of the leading commercial solutions, assessing their portfolios and business maturity, while highlighting major open innovation collaborations with prominent pharmaceutical and biotechnology companies. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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19 pages, 650 KB  
Article
LEMAD: LLM-Empowered Multi-Agent System for Anomaly Detection in Power Grid Services
by Xin Ji, Le Zhang, Wenya Zhang, Fang Peng, Yifan Mao, Xingchuang Liao and Kui Zhang
Electronics 2025, 14(15), 3008; https://doi.org/10.3390/electronics14153008 - 28 Jul 2025
Viewed by 2158
Abstract
With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time [...] Read more.
With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods. Full article
(This article belongs to the Special Issue Advanced Techniques for Multi-Agent Systems)
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17 pages, 2769 KB  
Article
Service-Based Architecture for 6G RAN: A Cloud Native Platform That Provides Everything as a Service
by Guangyi Liu, Na Li, Chunjing Yuan, Siqi Chen and Xuan Liu
Sensors 2025, 25(14), 4428; https://doi.org/10.3390/s25144428 - 16 Jul 2025
Viewed by 1020
Abstract
The 5G network’s commercialization has revealed challenges in providing customized and personalized deployment and services for diverse vertical industrial use cases, leading to high cost, low resource efficiency and management efficiency, and long time to market. Although the 5G core network (CN) has [...] Read more.
The 5G network’s commercialization has revealed challenges in providing customized and personalized deployment and services for diverse vertical industrial use cases, leading to high cost, low resource efficiency and management efficiency, and long time to market. Although the 5G core network (CN) has adopted a service-based architecture (SBA) to enhance agility and elasticity, the radio access network (RAN) keeps the traditional integrated and rigid architecture and suffers the difficulties of customizing and personalizing the functions and capabilities. Open RAN attempted to introduce cloudification, openness, and intelligence to RAN but faced limitations due to 5G RAN specifications. To address this, this paper analyzes the experience and insights from 5G SBA and conducts a systematic study on the service-based RAN, including service definition, interface protocol stacks, impact analysis on the air interface, radio capability exposure, and joint optimization with CN. Performance verification shows significant improvements of service-based user plane design in resource utilization and scalability. Full article
(This article belongs to the Special Issue Future Horizons in Networking: Exploring the Potential of 6G)
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19 pages, 1780 KB  
Article
A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network
by Rokin Maharjan, Korn Sooksatra, Tomas Cerny, Yudeep Rajbhandari and Sakshi Shrestha
Future Internet 2025, 17(7), 303; https://doi.org/10.3390/fi17070303 - 13 Jul 2025
Viewed by 1311
Abstract
Microservice is a popular architecture for developing cloud-native applications. However, decomposing a monolithic application into microservices remains a challenging task. This complexity arises from the need to account for factors such as component dependencies, cohesive clusters, and bounded contexts. To address this challenge, [...] Read more.
Microservice is a popular architecture for developing cloud-native applications. However, decomposing a monolithic application into microservices remains a challenging task. This complexity arises from the need to account for factors such as component dependencies, cohesive clusters, and bounded contexts. To address this challenge, we present an automated approach to decomposing monolithic applications into microservices. Our approach uses static code analysis to generate a dependency graph of the monolithic application. Then, a variational autoencoder (VAE) is used to extract features from the components of a monolithic application. Finally, the C-means algorithm is used to cluster the components into possible microservices. We evaluate our approach using a third-party benchmark comprising both monolithic and microservice implementations. Additionally, we compare its performance against two existing decomposition techniques. The results demonstrate the potential of our method as a practical tool for guiding the transition from monolithic to microservice architectures. Full article
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