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Nature-Inspired Optimization and Adaptive Bandwidth Selection for Computation Offloading in Cloud–Edge Ecosystems
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
The accelerating convergence of mobile computing, cloud services, and edge intelligence has fundamentally reshaped how computation, storage, and communication resources are provisioned and consumed. Modern digital ecosystems increasingly rely on dynamic computation offloading, adaptive bandwidth allocation, and intelligent optimization mechanisms to manage latency, energy consumption, and quality of service under heterogeneous and highly variable network conditions. Within this context, nature-inspired metaheuristic algorithms have emerged as a powerful paradigm for addressing complex, non-convex, and multi-objective optimization problems that traditional deterministic approaches struggle to solve efficiently. This research article develops an extensive and theoretically grounded investigation into adaptive bandwidth selection and computation offloading strategies for cloud–edge environments, with particular emphasis on biologically inspired optimization techniques. Drawing upon contemporary literature in mobile edge computing, cloud networking, and swarm intelligence, the article positions nature-inspired optimizers not merely as heuristic tools but as epistemologically significant frameworks that mirror decentralized intelligence and adaptive behavior observed in natural systems.
The study is conceptually anchored in recent advances in network bandwidth optimization for cloud data upload and access, particularly those employing dingo-inspired optimization strategies to reduce processing time and enhance user experience in cloud-centric systems (Alikhan et al., 2023). This approach is contextualized alongside other prominent nature-inspired algorithms, such as the Artificial Gorilla Troops Optimizer, which exemplifies the broader trend toward biologically grounded global optimization mechanisms capable of navigating high-dimensional search spaces (Abdollahzadeh et al., 2021). By synthesizing these perspectives, the article constructs a unified theoretical narrative that links adaptive bandwidth selection, computation offloading decisions, and energy-aware resource allocation within a single optimization-centric framework.
Methodologically, the research adopts a qualitative and interpretive design grounded in comparative theoretical analysis rather than empirical experimentation. The methodology explicates how algorithmic principles derived from animal behavior, social hierarchies, and collective intelligence can be mapped onto network decision-making processes in cloud–edge systems. Particular attention is given to how these algorithms address uncertainty, partial observability, and dynamic system states, drawing conceptual parallels with decision-theoretic models such as partially observable Markov decision processes (Chades et al., 2021). The results section offers a descriptive synthesis of findings reported across the literature, highlighting consistent patterns regarding latency reduction, processing time minimization, and improved bandwidth utilization when adaptive and bio-inspired strategies are employed (Alikhan et al., 2023; Chen et al., 2021).
The discussion extends beyond performance metrics to critically examine the theoretical implications of adopting nature-inspired optimization in communication networks. It interrogates debates surrounding algorithmic interpretability, scalability, and reproducibility, while also addressing limitations related to computational overhead and parameter sensitivity. Furthermore, the article situates these optimization approaches within broader socio-technical considerations, including sustainability, energy efficiency, and the evolving role of edge intelligence in future network architectures (Ernest & Madhukumar, 2024; Liu et al., 2023). The conclusion synthesizes these insights, arguing that nature-inspired optimization represents not a transient methodological trend but a foundational paradigm for the next generation of adaptive cloud–edge systems.
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References
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