RIoTBench: An IoT benchmark for distributed stream processing systems

Summary The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates...

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Veröffentlicht in:Concurrency and computation Jg. 29; H. 21
Hauptverfasser: Shukla, Anshu, Chaturvedi, Shilpa, Simmhan, Yogesh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 10.11.2017
Schlagworte:
Analytics
benchmark
Benchmarks
big data applications
Data centers
Data processing
Data transmission
dataflows
Decision analysis
Decision making
Digital media
distributed stream processing
Empirical analysis
Internet of Things
Life cycle engineering
Messages
performance evaluation
Performance measurement
Preprocessing
Real time
Resource scheduling
Sensors
ISSN:1532-0626, 1532-0634
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Zusammenfassung:Summary The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real‐time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench, a real‐time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed‐loop IoT decision‐making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10 000 messages/second from up to 3 million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.4257