Network-Aware Cloud Integration with Uncertainty-Gated Pareto Decisions: A Formal Cognitive Architecture with Exact Computational Verification

Cloud integration can be economically attractive yet operationally unsuitable when dependent services exchange traffic over constrained network paths. This study develops a network-aware extension of a time-dependent, three-objective integration model and specifies a cognitive architecture for admitting, deferring, or escalating Pareto-based decisions. Strategic portfolio selection is separated from operational routing; infrastructure and service-dependency graphs connect both levels. The central mechanism evaluates whether the recommended action remains consistent across explicitly defined uncertainty scenarios, while independently checking service constraints, telemetry quality, authorization, and execution budgets. Numerical evidence is separated into an established synthetic portfolio benchmark, an independent reconstruction from its published inputs, and new deterministic network and admission-rule fixtures. Enumeration of the 18-system benchmark confirms 83,657 feasible portfolios and 446 nondominated solutions. Reanalysis of 30 paired run records preserves the original qualitative trade-off: a reference-direction NSGA-III-type baseline improves IGD+ and exact-front coverage, whereas NSGA-II uses less recorded CPU time. This comparison is a secondary reanalysis of offline optimizer records, not a measurement of real-time performance. A new three-service, two-route fixture contains 16 configurations, of which three satisfy the network-aware requirements; an additive compromise using the default route violates the specified end-to-end delay bound. A seeded stress test over 2547 synthetic instances, built from three service-dependency graph families on networks of two or three parallel logical routes between a local and a cloud domain, with traffic shifts, congestion, and route failures, reproduces this effect beyond the single fixture. A nine-scenario admission test yields recommendation agreement of 2/3, correctly withholding an automatic action at a threshold of 0.8, while a narrowed three-scenario set yields full agreement. These are deterministic model-based checks, not measured improvements in a deployed network; no trained predictor, emulation, or closed-loop controller is evaluated. The contribution is therefore formal and computational: an auditable connection between portfolio optimization, service dependencies, and decision-specific uncertainty. A separately identified evaluation protocol defines the learning, telemetry, ablation, and real-traffic evidence still required to establish closed-loop effectiveness.