Abstract

IoT gateway–cloud systems support large-scale sensing, monitoring, and control applications, but must operate under finite buffering, dynamic traffic demands, and service interruptions caused by gateway and cloud failures. These challenges can lead to backlog accumulation, congestion, and degraded service performance, making admission-threshold selection an important reliability-management problem. This paper investigates reliability-aware admission-threshold selection for finite-buffer systems with service interruptions, motivated by IoT gateway–cloud architectures. A finite level-dependent quasi-birth-and-death (LD-QBD) model is developed to jointly capture probabilistic admission control, finite buffering, and gateway–cloud failures. Exact stationary analysis yields a multidimensional performance-characterization framework based on effective service deliverability, congestion-regime probability, saturation probability, and soft normalized headroom. The admission threshold is shown to govern the trade-off between service deliverability and congestion protection under failure-induced backlog dynamics. To address this trade-off, two complementary threshold-selection paradigms are developed: a trade-off-driven weighted optimization approach and a constraint-based feasibility-enforcement approach. Numerical results show that the weighted formulation exhibits a well-defined knee point, whereas the constraint-based method produces reliability-aware threshold adjustments when operational constraints become active. The results further indicate that increasing traffic load or failure intensity generally requires more conservative admission policies. Although motivated by IoT gateway–cloud systems, the proposed framework is applicable to a broader class of finite-buffer service systems with unreliable resources.

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