2026· International Conference on Conceptual Structures· pp. 535-550· 0 citations· 21 references
Computer Science
TL;DR
This paper presents an architecture for data management and I/O provisioning that supports the execution of high-performance data pipelines across the cloud–edge continuum and combines federated data management, intelligent data placement, and continuum-aware resource orchestration within a unified platform.
A resource-aware multi-application operator placement method that optimizes both end-to-end latency and network usage, while meeting QoS constraints and application owners’ preferences in heterogeneous cloud-edge environments is proposed.
Simin Ghasemi-Falavarjani, B. S. Ghahfarokhi, M. Nematbakhsh et al.· Cluster Computing· 1 citation
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations
The proposed approach separates the research-infrastructure layer, which exposes and manages distributed resources, from the application layer, where Cyber-Physical workflows are organized according to an Edge-Fog-Cloud pattern in which placement, timing, and data provenance are treated as first-class experimental concerns.
Fabio Orazio Mirto, Giuseppe Tricomi, L. D’Agati et al.· arXiv.org· 0 citations
Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.
Narendra Karmarkar· International Journal of Dat...· 0 citations
Cloud native data pipelines have become an enabling ingredient of the modern enterprise analytics to fulfill the ever-increasing demand of a scale-loving, resilient, and real-time processing of a wide range of data sources. Organizations currently produce large amounts of structured, semi-structured, and unstructured data in transactional systems, Internet of Things (IoT) platforms, digital channels, and data sources that are external (Bank of America 2017). Old monolithic data integration architectures are designed to provide batch-oriented processing and static infrastructure capabilities have challenges satisfying low latency, scale on demand, and 24/7 requirements. Reactively, cloud-native paradigms, including the foundations of microservices, container orchestration, event-driven architectures, and managed cloud services have caused a rethinking of the data pipeline design, deployment and operation. This article provides an in-depth analysis of cloud-native pipeline data to enterprise analytics along with their main architectural concepts and processing models as well as operational aspects that fall within the professional scope of IEEE publications. The research paper summarizes the literature and business methodologies to present a reference model which brings together data ingestion, stream processing, batch processing, storage, governance, and analytics consumption layers. Special concern is opened to the contributions of containerization, orchestration platforms, and serverless computing towards facilitation of elasticity and fault tolerance. The paper also examines design patterns like Lambda architecture and Kappa architecture, data mesh theory and metadata-based orchestration, with an emphasis on its application to the large enterprise environment. An organized approach to the design and deployment of cloud-native data pipelines with the inclusion of data quality management, security controls, observability, and cost optimization is suggested. Throughput, latency and scalability modeling mathematical formulations are proposed in order to facilitate capacity planning and performance measurement. Representative enterprise workloads as shown through experiment results exhibit evident increases in data processing latency, pipeline reliability and operational efficiency over traditional architectures. These findings are placed in context to the discussion of the broader transformation efforts at enterprises, whereas the conclusion provides recommendations on future research opportunities, such as autonomous pipeline optimization and AI-based orchestration.
Ethan Williams· International Journal of App...· 0 citations
Centralized data operations are often using in Small and medium-sized enterprises (SMEs) for easy data management, but there suffering from few limitations like cloud platform integration with bigdata yet many still face difficulties integrating cloud platforms with big-data capabilities in a scalable and governed manner. To address the problems, this communication presents an Adaptive Cloud -Big-Data Enablement Framework (ACBDEF), which is a realistic mechanism of SME digital data transformation. This framework consists of five main steps, which are technological infrastructure, data governance and compliance, organizational capability development, environmental alignment, and intelligence/value extraction into a common architecture. One of the key elements in this work is the Adaptive Migration Engine (AME) which is used to assess dynamically workloads, the parameters such as data characteristics, regulatory constraints, the cost-performance metrics are used to decide on the optimal deployment in cloud premises, or in hybrid environments. The adaptive decision process is beneficial in assisting SMEs in mitigating technical and organizational issues in enhancing the efficiency, security, and analytical responsiveness. The proposed mechanism brings into line theoretical adoption factors with actionable implementation which provides a structured model for supporting SMEs to achieve sustainable and data-driven cloud transformation.
B. Madhu Uthej, Dudde Lohith, Atluru Sai Charan Reddy et al.· 2026 7th International Confe...· 0 citations
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