TinTiN: Travelling in time (if necessary) to deal with out-of-order data in streaming aggregation

Paper in proceeding, 2020

Cyber-Physical Systems (CPS) rely on data stream processing for high-throughput, low-latency analysis with correctness and accuracy guarantees (building on deterministic execution) for monitoring, safety or security applications.
The trade-offs in processing performance and results' accuracy are nonetheless application-dependent. While some applications need strict deterministic execution, others can value fast (but possibly approximated) answers.
Despite the existing literature on how to relax and trade strict determinism for efficiency or deadlines, we lack a formal characterization of levels of determinism, needed by industries to assess whether or not such trade-offs are acceptable.
To bridge the gap, we introduce the notion of D-bounded eventual determinism, where D is the maximum out-of-order delay of the input data.
We design and implement TinTiN, a streaming middleware that can be used in combination with user-defined streaming applications, to provably enforce D-bounded eventual determinism.
We evaluate TinTiN with a real-world streaming application for Advanced Metering Infrastructure (AMI) monitoring, showing it provides an order of magnitude improvement in processing performance, while minimizing delays in output generation, compared to a state-of-the-art strictly deterministic solution that waits for time proportional to D, for each input tuple, before generating output that depends on it.