Approximate Fault Tolerance for Edge Stream Processing

Existing distributed stream processing systems generally guarantee fault tolerance by switching to standby machines and reprocessing lost data. In edge computing environments, however, we have to duplicate each edge for this conventional approach. This duplication cost increases sharply with expansion in the system scale. To solve this problem, we propose an approach to support approximate fault tolerance without edge duplication. We focus on environmental monitoring applications and utilize the correlation between sensors. In this paper, we assume that each edge estimates missing data from the observed data and aggregates them approximately. We provide a method to estimate the outputs of failed edges taking care of the uncertainty of the processing results at each edge. Our method allows the server to continue processing without waiting for the recovery of failed edges. We also show that the validity of our method by experiments using synthetic data.