02185nas a2200217   4500000000100000008004100001260005900042653003100101653001700132653002800149653002300177100002000200700001900220700001500239700001800254700001600272245007700288856004400365520154000409020001801949       2023                            d  bAssociation for Computing MachineryaNew York, NY, USA10aadaptive stream processing10aapache flink10aapproximate computation10astateful functions1 aJoão Francisco1 aMiguel Coimbra1 aPedro Neto1 aFelix Freitag1 aLuís Veiga00aStateful Adaptive Streams with Approximate Computing and Elastic Scaling  uhttps://doi.org/10.1145/3555776.35778583 aThe model of approximate computing can be used to increase performance or optimize resource usage in stream and graph processing. It can be used to satisfy performance requirements (e.g., throughput, lag) in stream processing by reducing the effort that applications need to process datasets. There are currently multiple stream processing platforms, and most of them do not natively support approximate results. A recent one, Stateful Functions, is an API that uses Flink to enable developers to easily build stream and graph processing applications. It also retains Flink’s features like stateful computations, fault-tolerance, scalability, control events and its graph processing library Gelly. Herein we present Approxate, an extension over this platform to support approximate results. It can also support more efficient stream and graph processing by allocating available resources adaptively, driven by user-defined requirements on throughput, lag, and latency. This extension enables flexibility in computational trade-offs such as trading accuracy for performance. The user can choose which metrics should be guaranteed at the cost of others, and/or the accuracy. Approxate incorporates approximate computing (using load shedding) with adaptive accuracy and resource manegement in state-of-the-art stream processing platforms, which are not targeted in other relevant related work. It does not require significant modifications to application code, and minimizes imbalance in data source representation when dropping events.  a9781450395175