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