Cost-Effective Sharing of Streaming Dataflows for IoT Applications

Abstract

Internet of Things (IoT) applications are often designed as dataflows that analyze sensor data in real-time to make decisions. Stream processing systems like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Apache Storm</i> execute these on Cloud infrastructure. As IoT applications within shared data environments like smart cities grow, they will duplicate tasks like pre-processing and analytics. This offers the opportunity to collaboratively reuse the outputs of overlapping dataflows, improving the resource efficiency on Clouds. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dataflow reuse algorithms</i> that when given a submitted dataflow, identify the intersection of reusable tasks and streams from existing dataflows to form a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">merged dataflow</i> , with guaranteed equivalence of their output streams. Algorithms to unmerge dataflows when they are removed, and defragment partially reused dataflows are also proposed. We implement these algorithms for the Storm fast-data platform, and validate their performance and resource savings using 86 real and synthetic dataflows from eScience and IoT domains. Our reuse strategies reduce the number of running tasks by 34–45 percent and the cumulative CPU usage by 29–63 percent. Including defragmentation of incremental dataflows achieves a monetary savings on Cloud resources of 36–44 percent compared to dataflows without reuse, and has limited redeployment overheads.