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To extract value from evergrowing volumes of data, coming from a number ofdifferent sources, and to drive decision making, organizations frequentlyresort to the composition of data processing workflows, since they areexpressive, flexible, and scalable. The typical workflow model enforces stricttemporal synchronization across processing steps without accounting the actualeffect of intermediate computations on the final workflow output. However, thisis not the most desirable behavior in a multitude of scenarios. We identify aclass of applications for continuous data processing where workflow outputchanges slowly and without great significance in a short-to-medium time window,thus wasting compute resources and energy with current approaches.  To overcome such inefficiency, we introduce a novel workflow model, forcontinuous and data-intensive processing, capable of relaxing triggeringsemantics according to the impact input data is assessed to have on changingthe workflow output. To assess this impact, learn the correlation between inputand output variation, and guarantee correctness within a given tolerated errorconstant, we rely on Machine Learning. The functionality of this workflow modelis implemented in SmartFlux, a middleware framework which can be effortlesslyintegrated with existing workflow managers. Experimental results indicate weare able to save a significant amount of resources while not deviating theworkflow output beyond a small error constant with high confidence level.

Smart Scheduling of Continuous Data-Intensive Workflows with Machine Learning Triggered Execution