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 brokerworker 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 RaspberryPi 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.