Integrating AI and Cloud Technologies for Scalable, Low-Latency Edge Computing in Enterprise Workloads

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Published: 2025-05-05
DOI: https://doi.org/10.15662/IJRPETM.2025.0803007
Keywords:
Edge Computing, Artificial Intelligence (AI), Cloud-Edge Integration, Low-Latency Systems, Enterprise Workloads, Scalable Computing Architectures, Distributed Intelligence

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Ashok Mohan Chowdhary Jonnalagadda
Hilmar, USA

Abstract

The digital transformation, the growing number of connected devices, and the demand for real-time decision-making are driving unprecedented growth in data-intensive workloads and the increasing latency sensitivity of workloads in enterprises across various industries. The existing cloud computing, although it is very robust regarding scalability and centralized resource control, is in most cases unable to address the low latency and high reliability issues required by mission-critical applications. The concept of edge computing has become a supporting paradigm, placing computation within closer proximity to the sources of data; however, the lack of resource capacity is not easily manageable in large-scale workloads of enterprises. A combination of artificial intelligence (AI) and the cloud and edge systems provides the avenue towards scalable, adaptive, and low-latency computing.


 


This article discusses the intersection of AI and cloud technology in supporting a scalable edge computing architecture that can meet the needs of an enterprise. We discuss the use of AI-based orchestration to optimize workload delivery between cloud and edge to improve efficiency and responsiveness. Some of the architectural models are discussed, and the trade-offs between cloud-centric, edge-centric, and hybrid deployments are identified. The performance aspects, such as the latency, scalability, and fault tolerance, are considered, keeping in mind the enterprise scale requirements. Moreover, the paper also discusses the most significant challenges, including data security, privacy, and compliance with regulations in AI-enabled edge environments, which are the main determinants of the scale of adoption.


 


We emphasize that the combination of AI, cloud, and edge computing provides a synergistic platform that can fulfill the requirements of enterprises in real-time intelligence, scalability, and operational efficiency. We show that hybrid AI-cloud-edge architectures can be deployed to minimize latency, besides offering a resilient and cost-effective platform in various industries such as healthcare, finance, and manufacturing. Lastly, the paper also recognizes persistent issues like interoperability, the complexity of orchestration, and security weaknesses, and suggests future research directions. The contribution of this work is the holistic look at how enterprises can use AI-cloud-edge integration to develop sustainable and high-performance computing infrastructures.

Article Details

Issue

Vol. 8 No. 3 (2025): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Integrating AI and Cloud Technologies for Scalable, Low-Latency Edge Computing in Enterprise Workloads. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(3), 12110-12120. https://doi.org/10.15662/IJRPETM.2025.0803007

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