TSAE: A Service Availability Evaluation Method for IIoT Under Dynamic Recovery

Service availability (SA) is essential for maintaining productivity and operational continuity in Industrial Internet of Things (IIoT) systems. However, accurately evaluating availability is challenging due to the complexity of dynamic recovery techniques integrated failure detection, localization, and rerouting restoration. These processes involve state-dependent behaviors and interactions across multiple network layers, making it difficult to pre-enumerate all recovery paths before failures occur. Thus, it is crucial to develop availability models that account for the dynamic coupling between service failure and recovery factors to achieve more accurate evaluations. To address this, we propose a novel two-stage state-transition availability evaluation method (TSAE) to quantify dynamic service availability in IIoT systems. In the first stage, fault conditions of network components and service mapping states are sampled to identify events that lead to service outages. In the second stage, a set of transition rules is defined to model the complex interactions between components and services during dynamic recovery processes. The effectiveness and accuracy of the proposed method are validated using two typical network scenarios. Extensive sensitivity experiments on a large-scale network case, considering factors, such as recovery priority, resource demand-supply ratio, and recovery performance, reveal key availability bottlenecks under various deployment configurations. Furthermore, tradeoffs between global and local recovery strategies under varying failure conditions are discussed, providing insights for network designer to select options that balance service continuity and resource efficiency.