• Priyanka Singh
• Nitya Khare

Fog computing extends cloud capabilities by decentralizing computational, storage, and networking resources, bringing them closer to IoT devices. This proximity enables low-latency processing, real-time analytics, and efficient data handling, making it ideal for applications like smart cities, healthcare monitoring, and industrial automation. However, the distributed nature of fog computing introduces several challenges, particularly in managing constrained resources across heterogeneous fog nodes. One of the primary challenges is the efficient management of Service Level Agreements (SLAs), which depend on dynamic Quality of Service (QoS) requirements. SLAs must balance user expectations, application performance, and provider profitability, making it difficult to establish precise service metrics. Additionally, fog environments face scalability issues due to the increasing number of connected devices, requiring adaptive resource allocation strategies. Interoperability is another concern, as fog nodes often operate on diverse hardware and software platforms, complicating seamless integration. Furthermore, ensuring reliability in resource-constrained and geographically dispersed fog networks remains a critical hurdle. To address these challenges, this study explores intelligent resource management strategies designed to optimize constrained, heterogeneous, and distributed fog resources. The proposed approach leverages dynamic application placement algorithms, adaptive workload distribution, and predictive resource allocation to enhance efficiency. By improving resource utilization and service continuity, the framework ensures high performance while minimizing latency and energy consumption.Ultimately, this research aims to enhance fog computing networks by enabling scalable, reliable, and interoperable service delivery. The findings contribute to advancing real-time IoT applications, ensuring seamless operations in dynamic fog environments. Through intelligent resource management, fog computing can achieve its full potential as a robust extension of cloud infrastructure

Keywords—Fog Computing, Edge Computing, Resource Management, Service Level Agreement (SLA), Quality of Service (QoS), Application Placement, Scalability, Interoperability, Distributed Systems, Intelligent Computing.

"Hybrid AI-Based Framework for Optimized Resource Management in Fog Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.j182-j186, May-2025, Available :http://www.jetir.org/papers/JETIR2505A20.pdf

"Hybrid AI-Based Framework for Optimized Resource Management in Fog Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppj182-j186, May-2025, Available at : http://www.jetir.org/papers/JETIR2505A20.pdf