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Benchmarking Distributed Stream Processing Platforms for IoT Applications

Shukla, Anshu and Simmhan, Yogesh (2017) Benchmarking Distributed Stream Processing Platforms for IoT Applications. In: PERFORMANCE EVALUATION AND BENCHMARKING: TRADITIONAL - BIG DATA - INTERNET OF THINGS, TPCTC 2016, SEP 05-09, 2016, New Delhi, INDIA, pp. 90-106.

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Official URL: http://dx.doi.org/10.1007/978-3-319-54334-5_7


Internet of Things (IoT) is a technology paradigm where millions of sensors monitor, and help inform or manage, physical, environmental and human systems in real-time. The inherent closed-loop responsiveness and decision making of IoT applications makes them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) are becoming essential components of any IoT stack, but the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT data streams and applications. Here, we develop a benchmark suite and performance metrics to evaluate DSPS for streaming IoT applications. The benchmark includes 13 common IoT tasks classified across functional categories and forming micro-benchmarks, and two IoT applications for statistical summarization and predictive analytics that leverage various dataflow patterns of DSPS. These are coupled with stream workloads from real IoT observations on smart cities. We validate the benchmark for the popular Apache Storm DSPS, and present the results.

Item Type: Conference Paper
Series.: Lecture Notes in Computer Science
Publisher: 10.1007/978-3-319-54334-5_7
Additional Information: 8th TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC), New Delhi, INDIA, SEP 05-09, 2016 Copy right for this article belongs to the SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 12 Jan 2018 06:30
Last Modified: 22 Oct 2018 10:36
URI: http://eprints.iisc.ac.in/id/eprint/58783

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