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Architectural Intelligence and Adaptive Control in Fog–Edge–SDN–IoT Ecosystems: Performance, Security, and Predictive Load Orchestration
Issue Vol. 2 No. 01 (2025): Volume 02 Issue 01 --- Section Articles
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Abstract
The rapid expansion of Internet of Things (IoT) ecosystems has precipitated a paradigmatic transformation in contemporary networked systems, compelling a departure from centralized cloud-centric architectures toward decentralized, intelligence-driven paradigms encompassing fog computing, edge computing, and software-defined networking (SDN). This transformation is not merely infrastructural but epistemic, redefining how computation, control, security, and performance optimization are conceptualized and operationalized across heterogeneous and latency-sensitive environments. Within this evolving technological landscape, SDN controllers, predictive intelligence models, and adaptive load-balancing mechanisms have emerged as foundational enablers of scalability, resilience, and efficiency. This research article develops a comprehensive and theoretically grounded investigation into adaptive control and orchestration mechanisms within fog–edge–SDN–IoT ecosystems, with particular emphasis on controller performance, predictive server load distribution, security-aware intelligence, and dynamic migration strategies.
Grounded strictly in contemporary scholarly literature, this study integrates insights from performance evaluations of SDN controllers, notably the Ryu controller under diverse network scenarios, enhanced load-balancing frameworks employing multi-threshold switch migration, and advanced predictive models utilizing long short-term memory (LSTM) architectures for multimedia IoT environments (Montazerolghaem and Imanpour, 2025; Kazemiesfeh et al., 2025; Imanpour et al., 2025). Simultaneously, the research contextualizes these architectural advances within broader fog and edge computing paradigms, exploring resource management strategies, mobility-induced service migration, task offloading mechanisms, and industrial IoT security frameworks (Gadasin et al., 2018; Mostafa et al., 2020; Rejiba et al., 2019; Tange et al., 2020). The integration of intelligent intrusion detection mechanisms, particularly attention-based CNN-BiLSTM architectures, further expands the analytical scope by embedding security as a co-equal dimension of performance optimization in IoT networks (Naeem et al., 2025).
Methodologically, the study adopts a qualitative-analytical research design rooted in interpretive synthesis, comparative architectural analysis, and theoretically informed performance interpretation. Rather than relying on experimental replication, the research interrogates reported findings across the literature to derive systemic insights into how predictive intelligence, adaptive thresholds, and controller-centric orchestration reshape the operational logic of distributed computing environments. The results reveal that controller responsiveness, predictive load anticipation, and migration-aware control policies collectively constitute a new class of architectural intelligence, enabling fog–edge–IoT systems to transcend static optimization models.
The discussion advances a critical theoretical synthesis, juxtaposing centralized versus decentralized control philosophies, reactive versus predictive management strategies, and performance-centric versus security-aware optimization frameworks. Limitations pertaining to scalability, interpretability of deep learning models, and cross-layer coordination are examined, alongside prospective research directions emphasizing explainable AI, federated learning, and autonomic SDN-fog convergence. By articulating a unified conceptual framework, this article contributes to the scholarly discourse on next-generation distributed systems, offering a robust intellectual foundation for future research and system design.
Keywords
References
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