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In recent years, the deployment of large-scale Internet of Things (IoT)applications has given rise to edge federations that seamlessly interconnectand leverage resources from multiple edge service providers. The requirement ofsupporting both latency-sensitive and compute-intensive IoT tasks necessitatesservice resilience, especially for the broker nodes in typical broker-workerdeployment designs. Existing fault-tolerance or resilience schemes often lackrobustness and generalization capability in non-stationary workload settings.This is typically due to the expensive periodic fine-tuning of models requiredto adapt them in dynamic scenarios. To address this, we present a confidenceaware resilience model, CAROL, that utilizes a memory-efficient generativeneural network to predict the Quality of Service (QoS) for a future state and aconfidence score for each prediction. Thus, whenever a broker fails, we quicklyrecover the system by executing a local-search over the broker-worker topologyspace and optimize future QoS. The confidence score enables us to keep track ofthe prediction performance and run parsimonious neural network fine-tuning toavoid excessive overheads, further improving the QoS of the system. Experimentson a Raspberry-Pi based edge testbed with IoT benchmark applications show thatCAROL outperforms state-of-the-art resilience schemes by reducing the energyconsumption, deadline violation rates and resilience overheads by up to 16, 17and 36 percent, respectively.

CAROL: Confidence-Aware Resilience Model for Edge Federations