Abstract

Edge computing paradigms have emerged as a transformative approach for enabling real-time big data analytics at the network edge, facilitating low-latency processing and efficient utilization of resources. This paper investigates edge computing paradigms tailored for real-time big data analytics at the network edge. We explore the fundamental principles and components of edge computing architectures, including edge devices, edge computing nodes, and edge analytics frameworks. Additionally, we discuss key challenges and opportunities in deploying real-time big data analytics at the network edge, such as data locality, network bandwidth constraints, and edge resource constraints. Through empirical evaluation and case studies, we assess the performance, scalability, and efficiency of different edge computing paradigms for real-time big data analytics in diverse network edge environments. Furthermore, we discuss emerging trends, best practices, and future research directions in leveraging edge computing for real-time big data analytics at the network edge. This study aims to provide insights and guidelines for practitioners and researchers in harnessing edge computing paradigms to enable efficient and responsive big data analytics at the network edge.

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