Leveraging Edge and Fog Computing for Efficient Big Data Processing with Machine Learning Integration
DOI:
https://doi.org/10.15680/IJCTECE.2020.0301001Keywords:
Edge Computing, Fog Computing, Big Data Processing, Machine Learning, Distributed Systems, Data Analytics, Real-time Computing, Internet of Things (IoT), Data Offloading, Latency ReductionAbstract
Edge and fog computing offer unique solutions for the challenges faced in big data processing by distributing computing resources closer to data sources. This proximity enables faster data processing, reduces latency, and alleviates network bandwidth constraints. Machine learning (ML) integration in these environments enhances decision-making, real-time analytics, and predictive capabilities. The convergence of edge, fog computing, and ML addresses critical concerns in modern data processing workflows. This paper explores the synergy between edge, fog, and ML in improving the efficiency of big data processing. It discusses the architecture, benefits, challenges, and practical applications of this approach. Through case studies and methodologies, the paper illustrates how ML models are deployed across edge and fog nodes to optimize resource utilization, improve system performance, and reduce processing times. Additionally, we provide an overview of current research and future directions for further integration of machine learning with edge and fog computing paradigms
References
Provide citations for all the papers, articles, and books you referred to throughout your paper. Ensure you follow a consistent citation style (e.g., APA, IEEE). Here are some sample references:
1. Zhang, H., & Zhang, X. (Machine Learning for Edge Computing: Techniques and Applications. Springer.
2. Aazam, M., & Huh, E. Fog Computing for Big Data Processing: Architecture and Applications. Elsevier.
3. Bonomi, F., Milito, R., Natarajan, P., & Zhang, J. Fog Computing and Its Role in the Internet of Things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 13-16.
