Decision-Centric Architectures for Intelligent and Networked Wireless Computing Environments Operating at Scale and Uncertainty
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P115Keywords:
Decision-Centric System Engineering, Architectural Decision Modeling, Distributed System Control, Large-Scale System Architecture, System Analytics Under Uncertainty, Adaptive Analytics Systems, O-RAN Near-Real-Time And Non-Real-Time RIC, Partially Observable Decision Models (POMDP), Scalable Control Plane Design, Uncertainty-Aware ControlAbstract
The issue of decision making has become a first-class concern in the large-scale wireless computing environments, which have volatility, partial observability and dynamic resource constraints. Conventional networked system designs focus on data flow, protocol layering and infrastructure abstractions, pushing the decision logic into either distributed heuristics or hard-coded control loops. Nonetheless, next-generation wireless ecosystems covering cloud-native radio access networks, edge computing fabrics, and AI-controlled control planes require architectural models with an outright focus on uncertainty-based decisions. The paper will suggest a decision-focused architecture that approaches the challenges of smart wireless environment at scale by combining decision modeling, uncertainty-sensitive analytics, and scalable control to mitigate the challenges associated with operations in such an environment. Decision-centric system engineering is a conceptualization of systems engineering, in which decision entities, policies, and adaptive reasoning mechanisms are considered the major organizing constructs. The proposed framework transforms sensing, inference, control, learning layers interactions defined by synthesizing ideas in distributed system control, partially observable decision model, and adaptive analytics system. At the heart of the solution lies formalization of architectural decision modeling (ADM), which allows representing the decision dependencies, time constraints and feedbacks in an informatic way. Moreover, probabilistic reasoning frameworks like Partially Observable Markov Decision Processes (POMDPs), are added as an attempt to ensure that there is some kind of uncertainty-aware control mechanisms that allow us to have a mathematically sound framework under which we optimize adaptive policies. The paper aligns the proposed architecture to current wireless computing environments, such as Open Radio Access Network (O-RAN) environments and multi-tier edge to cloud infrastructures. The focus is on scalable control plane design with the potential to handle heterogeneous nodes, dynamic workloads, and stochastic network condition. The analytical models show the effectiveness of decision-oriented abstractions to enhance resilience, predictability of latency and resource usage. Evaluation of small-scale but simulated large-scale wireless networks has shown significant improvements in the accuracy of the decisions, stability of the network and choosing the adaptive responsiveness across varying conditions. This study takes decisions to the architectural primitives to develop an integrated design framework of smart wireless systems. The results point to the fact that a combination of the decision models and analytics with control planes can help systems to be further accommodating to uncertainty, scalability pressures, and emergent behaviors. The paper provides theoretical background, architectural designs, and experimental evidence used in distributed AI systems, wireless networks, and large-scale cyber-physical systems.
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