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Advancing The Cloud-Edge Continuum: Architectures, Orchestration, And Future Directions In Edge-Native And Serverless Computing
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles --- Published Date: 2025-07-07
Abstract
The evolution of computing paradigms has witnessed a paradigmatic shift from centralized cloud infrastructures toward distributed, edge-centric architectures that enable low-latency, context-aware, and resource-efficient services. Edge computing, a pivotal component of this continuum, promises transformative capabilities in the domains of the Internet of Things (IoT), real-time analytics, and pervasive artificial intelligence applications. This paper provides a comprehensive, scholarly exploration of edge-native computing paradigms, serverless function deployment across the edge-cloud continuum, and the orchestration mechanisms underpinning these environments. Leveraging an extensive corpus of contemporary research, the study delineates theoretical foundations, architectural frameworks, and methodological approaches to optimize service placement, mobility-aware computing, and energy efficiency within distributed ecosystems. Furthermore, critical discussion is provided on the security, reliability, and scalability challenges inherent in edge-native deployments. By synthesizing perspectives from seminal research (Shi et al., 2016; Khan et al., 2019; Cao et al., 2020) and recent technological initiatives, this work highlights opportunities for the convergence of cloud-native principles with edge computing strategies. The paper emphasizes the role of predictive orchestration, function-as-a-service (FaaS) paradigms, and AI-driven resource allocation in facilitating a responsive, resilient, and context-sensitive computing continuum. The theoretical insights presented aim to inform the design of next-generation platforms that integrate cognitive, autonomous, and human-centric capabilities while addressing the practical constraints imposed by latency, bandwidth, and mobility. Finally, the study identifies gaps in current literature, proposing avenues for rigorous experimental validation and cross-disciplinary collaboration to advance the frontiers of edge computing research.
Keywords
References
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