Uncertainty-Calibrated Resource Envelopes and Distributed Admission Contracts for reliable Fog Computing
Keywords:
Admission control, conformal prediction, distributed systems, fog computing, resource allocation, service-level objectives, uncertainty calibrationAbstract
Fog platforms must admit short-lived workloads despite uncertain CPU, memory, network, accelerator, and energy demand. Reinforcement-learning schedulers can adapt over time, but their exploratory actions, training cost, opaque policies, and sensitivity to distribution shift complicate strict service-level objectives. This paper proposes UC-REACT, a non-reinforcement-learning framework that represents each request by an uncertainty-calibrated multi-resource envelope and allocates capacity through short-lived distributed admission contracts. A rolling residual model converts recent demand errors into conformal safety margins. Each fog domain then advertises a signed residual-capacity envelope rather than raw host telemetry. A requesting coordinator screens infeasible domains, proposes a contract containing resource bounds, duration, deadline, confidence target, price ceiling, and fallback rules, and commits only after independent capacity and policy checks. Contracts are revoked or resized when calibration fails, while critical work receives deterministic reserves. The paper develops the system model, formulation, envelope construction, contract protocol, bounded heuristic, failure semantics, security controls, and reproducible experimental design. It deliberately separates testable hypotheses from measured results; no performance numbers are fabricated. The new contribution changes the resource-management question from learning an action policy to certifying whether a placement remains safe under recent uncertainty
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References
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