Benchmarking Distributed Stream Processing Platforms for IoT Applications

TL;DR

This paper develops a comprehensive benchmark suite and performance metrics to evaluate distributed stream processing platforms specifically for IoT applications, using real-world data and diverse IoT tasks.

Contribution

It introduces a novel benchmark suite with 13 IoT tasks and two applications, validated on Apache Storm, to rigorously assess DSPS performance for IoT data streams.

Findings

Benchmark suite effectively evaluates DSPS performance.

Real IoT data used for realistic workload testing.

Empirical results highlight strengths and limitations of Apache Storm.

Abstract

Internet of Things (IoT) is a technology paradigm where millions of sensors monitor, and help inform or manage, physical, envi- ronmental and human systems in real-time. The inherent closed-loop re- sponsiveness and decision making of IoT applications makes them ideal candidates for using low latency and scalable stream processing plat- forms. Distributed Stream Processing Systems (DSPS) are becoming es- sential 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 per- formance metrics to evaluate DSPS for streaming IoT applications. The benchmark includes 13 common IoT tasks classified across various func- tional categories and forming micro-benchmarks, and two IoT applica- tions for statistical summarization and predictive analytics that leverage various dataflow compositional features of DSPS. These are coupled with stream workloads sourced from real IoT observations from smart cities. We validate the IoT benchmark for the popular Apache Storm DSPS, and present empirical results.