Relevant bibliographies by topics / Scalable Cloud Architecture

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Scalable Cloud Architecture'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Scalable Cloud Architecture"

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Dr. Pradeep Laxkar and Dr. Nilesh Jain. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2907–16. https://doi.org/10.32628/cseit25112764.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows. By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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Journal, of Global Research in Electronics and Communications. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." Journal of Global Research in Electronics and Communications 1, no. 4 (2025): 7–11. https://doi.org/10.5281/zenodo.15115138.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows.  By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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Santhosh, Podduturi. "Architectural Patterns for ML in Microservices & Cloud Architecture." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 1 (2023): 1–13. https://doi.org/10.5281/zenodo.15087171.

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Machine Learning (ML) is revolutionizing industries by enabling intelligent decision-making and automation. However, deploying ML models in modern cloud-native applications requires scalable, maintainable, and efficient architectural patterns. This paper explores architectural patterns that facilitate the seamless integration of ML into microservices and cloud-based ecosystems. It discusses various deployment models, including ML Model as a Service (MaaS), Event-Driven ML, Federated Learning, and Serverless ML, highlighting their advantages, challenges, and best practices.The paper delves into key considerations such as model scalability, versioning, security, real-time inference, and model drift management in microservices architectures. Analyze how cloud-native technologies, such as Kubernetes, serverless computing, and API gateways, can enhance the deployment and lifecycle management of ML models. Through this study, the aim is to provide software architects, ML engineers, and cloud practitioners with practical insights and strategies to design robust, scalable, and maintainable ML-driven microservices in cloud environments.
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Pushkar, Mehendale. "Scalable Architecture for Machine Learning Applications." Journal of Scientific and Engineering Research 11, no. 8 (2024): 111–17. https://doi.org/10.5281/zenodo.13753585.

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In the realm of Machine Learning (ML) applications, scalable architectures are crucial for addressing the challenges posed by large-scale ML tasks. This paper explores the integration of distributed computing and cloud infrastructures to ensure scalability, efficiency, and reliability while maintaining optimal performance and cost-effectiveness. It compares different cloud platforms, evaluates design patterns and architectural strategies, presents case studies from real-world ML deployments, and analyzes emerging technologies shaping the landscape of ML in the cloud. The paper concludes by providing best practices for designing and deploying scalable ML applications in the cloud, empowering ML practitioners with the knowledge and tools to build robust and scalable ML solutions.
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Pavankumar Yanamadala. "Demystifying cloud-native enterprise architecture: A framework for digital transformation in complex organizations." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1919–28. https://doi.org/10.30574/wjarr.2025.26.1.1231.

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This article presents a comprehensive framework for adopting cloud-native architecture within enterprise environments, addressing the significant challenges organizations face during digital transformation initiatives. The article examines the fundamental components of cloud-native systems—including containerization, microservices, and service mesh implementations—and their interconnections within a holistic architectural approach. Drawing from extensive industry implementation experiences, the article identifies critical patterns for migrating traditional enterprise architectures to distributed cloud-native models while maintaining security postures and regulatory compliance. The framework encompasses both technical architecture components and necessary organizational adaptations, providing actionable guidance for enterprises across various maturity levels. This article contributes to the enterprise architecture body of knowledge by bridging theoretical cloud-native concepts with practical implementation considerations, offering a structured pathway for organizations seeking resilient, scalable, and sustainable architectural transformation.
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G Renugadevi, M L Sharvesh, S Subhashini, and V S Vaishaal Krishna. "Scalable Cloud Execution Engines." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 10 (2024): 2521–28. http://dx.doi.org/10.47392/irjaeh.2024.0346.

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Scalability remains a major concern for many organizations, and as technology evolves expeditiously, the number of users utilizing it also increases rapidly. In this paper, we propose a novel approach to address this challenge through the implementation of a scalable cloud execution engine using a microservices architecture. By using this design we can achieve a system with loosely coupled and independently deployable methods. Also through this, we can achieve enhanced flexibility, scalability, and reusability in our application. Through experimenting with various execution engines it is evident that most of their design relies on monolithic architecture. However, this design poses potential challenges especially when the traffic experiences sudden spikes. Our proposed design provides practical insights for architects and developers seeking to design and deploy highly scalable cloud applications.
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Bhargav Mallampati. "Demystifying cloud-native microservices architecture for scalable applications." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1806–17. https://doi.org/10.30574/wjaets.2025.15.1.0422.

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Cloud-native microservices architecture represents a transformational shift in software development, enabling organizations to build resilient, scalable applications specifically designed for cloud environments through decomposed, independently deployable services. This architectural paradigm leverages cloud infrastructure capabilities including elastic scaling, self-healing, and managed services while emphasizing container-based deployments and orchestration platforms. Implementation rates are surging as enterprises recognize substantial benefits in resilience, time-to-market, and operational efficiency through cloud infrastructure integration. The architecture fundamentally alters application development by emphasizing service autonomy, loose coupling, container packaging, and infrastructure automation that substantially reduces cross-service dependencies while improving system maintainability. Containerization technologies and orchestration platforms like Kubernetes have emerged as essential cloud-native infrastructure components, dramatically improved deployment frequency and reducing infrastructure costs through selective scaling capabilities. Despite inherent challenges in data consistency, security, and operational complexity, mature patterns and cloud-specific technologies have evolved to address these concerns effectively. Case studies from industry leaders demonstrate the transformative potential of cloud-native microservices at scale, with documented improvements in deployment velocity, system reliability, and cloud resource utilization. Looking forward, the cloud-native microservices landscape continues evolving rapidly through AI-driven optimization, serverless computing integration, and enhanced security models that collectively promise greater automation, efficiency, and resilience for distributed applications in increasingly competitive digital environments.
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Vineel Muppa. "Cloud-native event processing: Designing scalable and resilient event-driven systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1053–63. https://doi.org/10.30574/wjaets.2025.15.1.0217.

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This article examines the principles and implementation strategies for event-driven cloud solutions, addressing the growing need for responsive, resilient, and automated systems in modern digital enterprises. The article presents a comprehensive analysis of event-driven architecture (EDA) patterns and their integration with cloud-native technologies, exploring the synergies between messaging systems, event brokers, and serverless computing frameworks. The article outlines architectural approaches for achieving optimal performance, fault tolerance, and operational efficiency while managing the inherent complexity of distributed event processing. Examining industry-leading platforms and real-world implementation scenarios, the article identifies best practices for designing, deploying, and maintaining event-driven cloud systems. Additionally, the article addresses the professional development landscape for specialists in this domain, providing guidance on career pathways and skill development. The Findings suggest that effective implementation of event-driven cloud architectures requires a balanced approach to technical design choices, organizational patterns, and continuous learning practices.
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Debski, Andrzej, Bartlomiej Szczepanik, Maciej Malawski, Stefan Spahr, and Dirk Muthig. "A Scalable, Reactive Architecture for Cloud Applications." IEEE Software 35, no. 2 (2018): 62–71. http://dx.doi.org/10.1109/ms.2017.265095722.

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Nikhil, Bhagat. "Optimizing Performance, Cost-Efficiency, and Flexibility through Hybrid Multi-Cloud Architectures." Journal of Scientific and Engineering Research 11, no. 4 (2024): 372–79. https://doi.org/10.5281/zenodo.14273093.

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Cloud Computing is the foundation of every modern company that is scalable, adaptable and economical. Hybrid multi-cloud environments, which combine private clouds, public clouds, and multiple cloud providers, represent the next generation for scaling cloud infrastructures. Hybrid cloud architecture lets organizations reap the security and control benefits of a private cloud while also taking advantage of the scalability and cost efficiency of a public cloud. Meanwhile, multi-cloud models avoid vendor lock-in, provide risk mitigation, and enable organizations to choose the best options from multiple providers. Hybrid and multi-cloud solutions together offer an integrated cloud architecture that maximizes usage, performance, and resilience. The paper delves into the advantages of hybrid and multi-cloud environments, including agility, cost efficiency and increased security. The paper also touches on organizational design considerations such as workload assignment, interoperability, security, and vendor selection. The paper provides guidelines for implementing hybrid multi-cloud environments where orchestration tools and automation play a vital role to facilitate the operations. Even though Hybrid multi-cloud architectures provide greater flexibility, they must be strategically designed, implemented and managed. By modernizing these environments, businesses can enhance performance, profitability, and agility, better preparing them to thrive in today’s competitive market.
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Dissertations / Theses on the topic "Scalable Cloud Architecture"

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Tropper, Robin. "Architecture and programming paradigm for a scalable, metamorphic and cloud-collaborative user environment." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28486.

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A growing number of enterprise applications on the Internet ranging from banking transactions to business management make use of real-time collaboration. Simultaneous access from any device to any set of applications shared among many users is a hot area of research and development. This thesis designed a thick-client for real-time collaboration supporting the applications development and interoperability. It introduces a new programming paradigm, algorithms and protocols to bring real-time collaboration to a web-based platform. Its component-oriented metamorphic architecture supports a run-time scalable multi-desktop environment connecting client applications through automated remote procedure call and the object request broker pattern while providing new mechanisms for dynamic resource loading. The new architecture supports unsolicited server control actions on the client using an event model to simulate interruptions and sustained user-activity during network failure. Results obtained validate the correctness of the approach and the feasibility of an extensible web-based platform for real-time collaboration.
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de, la Rúa Martínez Javier. "Scalable Architecture for Automating Machine Learning Model Monitoring." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280345.

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Last years, due to the advent of more sophisticated tools for exploratory data analysis, data management, Machine Learning (ML) model training and model serving into production, the concept of MLOps has gained more popularity. As an effort to bring DevOps processes to the ML lifecycle, MLOps aims at more automation in the execution of diverse and repetitive tasks along the cycle and at smoother interoperability between teams and tools involved. In this context, the main cloud providers have built their own ML platforms [4, 34, 61], offered as services in their cloud solutions. Moreover, multiple frameworks have emerged to solve concrete problems such as data testing, data labelling, distributed training or prediction interpretability, and new monitoring approaches have been proposed [32, 33, 65]. Among all the stages in the ML lifecycle, one of the most commonly overlooked although relevant is model monitoring. Recently, cloud providers have presented their own tools to use within their platforms [4, 61] while work is ongoing to integrate existent frameworks [72] into open-source model serving solutions [38]. Most of these frameworks are either built as an extension of an existent platform (i.e lack portability), follow a scheduled batch processing approach at a minimum rate of hours, or present limitations for certain outliers and drift algorithms due to the platform architecture design in which they are integrated. In this work, a scalable automated cloudnative architecture is designed and evaluated for ML model monitoring in a streaming approach. An experimentation conducted on a 7-node cluster with 250.000 requests at different concurrency rates shows maximum latencies of 5.9, 29.92 and 30.86 seconds after request time for 75% of distance-based outliers detection, windowed statistics and distribution-based data drift detection, respectively, using windows of 15 seconds length and 6 seconds of watermark delay.<br>Under de senaste åren har konceptet MLOps blivit alltmer populärt på grund av tillkomsten av mer sofistikerade verktyg för explorativ dataanalys, datahantering, modell-träning och model serving som tjänstgör i produktion. Som ett försök att föra DevOps processer till Machine Learning (ML)-livscykeln, siktar MLOps på mer automatisering i utförandet av mångfaldiga och repetitiva uppgifter längs cykeln samt på smidigare interoperabilitet mellan team och verktyg inblandade. I det här sammanhanget har de största molnleverantörerna byggt sina egna ML-plattformar [4, 34, 61], vilka erbjuds som tjänster i deras molnlösningar. Dessutom har flera ramar tagits fram för att lösa konkreta problem såsom datatestning, datamärkning, distribuerad träning eller tolkning av förutsägelse, och nya övervakningsmetoder har föreslagits [32, 33, 65]. Av alla stadier i ML-livscykeln förbises ofta modellövervakning trots att det är relevant. På senare tid har molnleverantörer presenterat sina egna verktyg att kunna användas inom sina plattformar [4, 61] medan arbetet pågår för att integrera befintliga ramverk [72] med lösningar för modellplatformer med öppen källkod [38]. De flesta av dessa ramverk är antingen byggda som ett tillägg till en befintlig plattform (dvs. saknar portabilitet), följer en schemalagd batchbearbetningsmetod med en lägsta hastighet av ett antal timmar, eller innebär begränsningar för vissa extremvärden och drivalgoritmer på grund av plattformsarkitekturens design där de är integrerade. I det här arbetet utformas och utvärderas en skalbar automatiserad molnbaserad arkitektur för MLmodellövervakning i en streaming-metod. Ett experiment som utförts på ett 7nodskluster med 250.000 förfrågningar vid olika samtidigheter visar maximala latenser på 5,9, 29,92 respektive 30,86 sekunder efter tid för förfrågningen för 75% av avståndsbaserad detektering av extremvärden, windowed statistics och distributionsbaserad datadriftdetektering, med hjälp av windows med 15 sekunders längd och 6 sekunders fördröjning av vattenstämpel.
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Books on the topic "Scalable Cloud Architecture"

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Practical Microsoft Azure IaaS: Migrating and Building Scalable and Secure Cloud Solutions. Apress, 2018.

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Cloud Native Infrastructure: Patterns for Scalable Infrastructure and Applications in a Dynamic Environment. O'Reilly Media, 2017.

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Biggs, John, and Vicente Herrera García. Building Intelligent Cloud Applications: Develop Scalable Models Using Serverless Architectures with Azure. O'Reilly Media, Incorporated, 2019.

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Aberer, Karl. Peer-To-Peer Data Management: For Clouds and Data-Intensive and Scalable Computing Environments. Springer International Publishing AG, 2011.

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Book chapters on the topic "Scalable Cloud Architecture"

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Aghilar, Potito, Vito Walter Anelli, Michelantonio Trizio, and Tommaso Di Noia. "Scalable Cloud-Native Pipeline for Efficient 3D Model Reconstruction from Monocular Smartphone Images." In Software Architecture. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42592-9_18.

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Mohapatra, Subasish, Banshidhar Majhi, and Srikanta Patnaik. "Sensor Cloud: The Scalable Architecture for Future Generation Computing." In Intelligent Computing, Networking, and Informatics. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1665-0_41.

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Cao, Dahai, Xiao Liu, and Yun Yang. "Novel Client-Cloud Architecture for Scalable Instance-Intensive Workflow Systems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41154-0_20.

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Oda, Kentaro, Toyohiro Hayashi, Shinobu Izumi, Tomohito Wada, and Shuichi Enokida. "Cloud Drive: A Computing Architecture for a Scalable Driving Safety Management System." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24660-9_9.

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Jarrous-Holtrup, Sezar, Folker Schamel, Kerstin Hofer, and Sergei Gorlatch. "A Scalable Cloud Deployment Architecture for High-Performance Real-Time Online Applications." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90539-2_26.

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Casturi, Rao, and Rajshekhar Sunderraman. "Script Based Migration Toolkit for Cloud Computing Architecture in Building Scalable Investment Platforms." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99133-7_4.

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Dutta, Arijit, Chinmaya Misra, Rabindra K. Barik, and Sushruta Mishra. "Enhancing Mist Assisted Cloud Computing Toward Secure and Scalable Architecture for Smart Healthcare." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_116.

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Westerlund, Magnus, Ulf Hedlund, Göran Pulkkis, and Kaj-Mikael Björk. "A Generalized Scalable Software Architecture for Analyzing Temporally Structured Big Data in the Cloud." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05951-8_53.

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Ouyang, Chun, Michael Adams, Arthur H. M. ter Hofstede, and Yang Yu. "Towards the Design of a Scalable Business Process Management System Architecture in the Cloud." In Conceptual Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00847-5_24.

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Basirat, Amir H., and Asad I. Khan. "Evolution of Information Retrieval in Cloud Computing by Redesigning Data Management Architecture from a Scalable Associative Computing Perspective." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17534-3_34.

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Conference papers on the topic "Scalable Cloud Architecture"

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Oliveira, Saulo Soares de, Carlos Henrique R. Souza, and Sergio T. Carvalho. "A Multiplayer Cloud Gaming Architecture for Scalable Physics." In 2024 IEEE Conference on Games (CoG). IEEE, 2024. http://dx.doi.org/10.1109/cog60054.2024.10645556.

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Jesus, Rui, José Frias, Pedro Gouveia, João Santinha, Luís Bastião Silva, and Carlos Costa. "DICOM Gateway Anonymizer: A Cloud architecture for a scalable research PACS." In 2024 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2024. http://dx.doi.org/10.1109/iscc61673.2024.10733721.

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Zhou, Feng, Xin Zhao, Jing Liu, et al. "Tighte: A Model for Campus Security Target Tracking in Edge Intelligent Computing Architecture." In 2024 IEEE 10th International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2024. http://dx.doi.org/10.1109/edgecom62867.2024.00025.

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Zhou, Feng, Zhaojin Lu, Hai Huang, et al. "Cande: A Model for Predicting the Risk of Campus Violence in an Edge Intelligent Computing Architecture." In 2024 IEEE 10th International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2024. http://dx.doi.org/10.1109/edgecom62867.2024.00017.

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Ratra, Karan Kumar, and Dhruv Kumar Seth. "AI-Driven Hybrid Edge-Cloud Architecture for Real-Time Big Data Analytics and Scalable Communication in Retail Supply Chains." In SoutheastCon 2025. IEEE, 2025. https://doi.org/10.1109/southeastcon56624.2025.10971468.

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Lora, Chandra Prakash, P. S. Pavan, and Nivedan Mahato. "Scalable Multi-Agent Reinforcement Learning Architectures for Cloud-Based Data Centers." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725653.

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Ileana, Marian, Pavel Petrov, and Vassil Milev. "Optimizing CRM Platforms with Distributed Cloud Architectures for Scalable Performance and Security." In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS). IEEE, 2024. https://doi.org/10.1109/isas64331.2024.10845520.

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Ozga, Wojciech, Patricia Sagmeister, Tamás Visegrády, and Silvio Dragone. "Wawel: Architecture for Scalable Attestation of Heterogeneous Virtual Execution Environments." In 2023 IEEE 16th International Conference on Cloud Computing (CLOUD). IEEE, 2023. http://dx.doi.org/10.1109/cloud60044.2023.00020.

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Kamburugamuve, Supun, Chathura Widanage, Niranda Perera, et al. "HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks." In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). IEEE, 2021. http://dx.doi.org/10.1109/cloud53861.2021.00036.

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Ismail, Bukhary Ikhwan, Mohd Nizam Mohd Mydin, and Mohammad Fairus Khalid. "Architecture of scalable backup service for private cloud." In 2013 IEEE Conference on Open Systems (ICOS). IEEE, 2013. http://dx.doi.org/10.1109/icos.2013.6735069.

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Reports on the topic "Scalable Cloud Architecture"

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Sinanan, Shawn, Amir Naser, Maribel Delatorre, Ahmet Soylemezoglu, and Garry Glaspell. Autonomous robotics development in Robot Operating System (ROS) 2 Humble. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49747.

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This report presents a novel Robot Operating System (ROS) 2–based simulation framework designed to facilitate the development and testing of an autonomous navigation stack. Elements of the navigation stack, including lidar odometry, simultaneous localization and mapping (SLAM), and frontier exploration, are discussed in detail. The key features of the navigation stack include real-time performance and scalable architecture. The simulation results were applied to a physical robot. As a result, the physical robot was able to autonomously map the interior of a building and to generate 2D occupancy and 3D point clouds of the environment.
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