Incremental dataflow execution, resource efficiency and probabilistic guarantees with Fuzzy Boolean nets

Based on the QoD notion, we propose a novel dataflow model, with framework (Fluxy), for orchestrating data-intensive processing steps, which communicate data via a NoSQL storage, and whose triggering semantics is driven by dynamic QoD constraints automatically defined for different datasets by means of Fuzzy Boolean Nets. These nets give probabilistic guarantees about the prediction of the cumulative error between consecutive dataflow executions. With Fluxy, we demonstrate how dataflows can be leveraged to respond to quality boundaries (that can be seen as SLAs) to deliver controlled and augmented performance, rationalization of resources, and task prioritization.