  The emergence of latency-critical AI applications has been supported by the
evolution of the edge computing paradigm. However, edge solutions are typically
resource-constrained, posing reliability challenges due to heightened
contention for compute and communication capacities and faulty application
behavior in the presence of overload conditions. Although a large amount of
generated log data can be mined for fault prediction, labeling this data for
training is a manual process and thus a limiting factor for automation. Due to
this, many companies resort to unsupervised fault-tolerance models. Yet,
failure models of this kind can incur a loss of accuracy when they need to
adapt to non-stationary workloads and diverse host characteristics. To cope
with this, we propose a novel modeling approach, called DeepFT, to proactively
avoid system overloads and their adverse effects by optimizing the task
scheduling and migration decisions. DeepFT uses a deep surrogate model to
accurately predict and diagnose faults in the system and co-simulation based
self-supervised learning to dynamically adapt the model in volatile settings.
It offers a highly scalable solution as the model size scales by only 3 and 1
percent per unit increase in the number of active tasks and hosts. Extensive
experimentation on a Raspberry-Pi based edge cluster with DeFog benchmarks
shows that DeepFT can outperform state-of-the-art baseline methods in
fault-detection and QoS metrics. Specifically, DeepFT gives the highest F1
scores for fault-detection, reducing service deadline violations by up to 37\%
while also improving response time by up to 9%.
