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IoTAbench: an Internet of Things Analytics Benchmark

Published: 31 January 2015 Publication History

Abstract

The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This ``Internet-of-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer ``Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform ``apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.

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  • (2023)TSM-Bench: Benchmarking Time Series Database Systems for Monitoring ApplicationsProceedings of the VLDB Endowment10.14778/3611479.361153216:11(3363-3376)Online publication date: 24-Aug-2023
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  • (2023)A Method to Evaluate the Performance of Predictors in Cyber-Physical SystemsProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583732(113-123)Online publication date: 15-Apr-2023
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cover image ACM Conferences
ICPE '15: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering
January 2015
366 pages
ISBN:9781450332484
DOI:10.1145/2668930
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 January 2015

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Author Tags

  1. benchmarking
  2. big data
  3. internet of things
  4. performance evaluation

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ICPE'15
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ICPE'15: ACM/SPEC International Conference on Performance Engineering
January 28 - February 4, 2015
Texas, Austin, USA

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ICPE '15 Paper Acceptance Rate 23 of 74 submissions, 31%;
Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

View all
  • (2023)TSM-Bench: Benchmarking Time Series Database Systems for Monitoring ApplicationsProceedings of the VLDB Endowment10.14778/3611479.361153216:11(3363-3376)Online publication date: 24-Aug-2023
  • (2023)Bang for the Buck: Evaluating the cost-effectiveness of Heterogeneous Edge Platforms for Neural Network WorkloadsProceedings of the Eighth ACM/IEEE Symposium on Edge Computing10.1145/3583740.3628437(94-107)Online publication date: 6-Dec-2023
  • (2023)A Method to Evaluate the Performance of Predictors in Cyber-Physical SystemsProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583732(113-123)Online publication date: 15-Apr-2023
  • (2023)Synthetic Agricultural Load Data Generation Using TimeGANs2023 North American Power Symposium (NAPS)10.1109/NAPS58826.2023.10318596(1-6)Online publication date: 15-Oct-2023
  • (2023)Situation-based Query Generation for Performance Evaluation of Cloud Managed IoT Applications2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00064(352-357)Online publication date: Jul-2023
  • (2023)Context Query Generation using Scene Graph approach2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00060(328-333)Online publication date: Jul-2023
  • (2023)SmartSPECPervasive and Mobile Computing10.1016/j.pmcj.2023.10180993:COnline publication date: 1-Jun-2023
  • (2023)MSDBench: Understanding the Performance Impact of Isolation Domains on Microservice-Based IoT DeploymentsBenchmarking, Measuring, and Optimizing10.1007/978-3-031-31180-2_3(35-52)Online publication date: 13-May-2023
  • (2022)PRIGM: Partial-Regression-Integrated Generic Model for Synthetic Benchmarks Robust to Sensor CharacteristicsIEICE Transactions on Information and Systems10.1587/transinf.2021EDL8113E105.D:7(1330-1334)Online publication date: 1-Jul-2022
  • (2022)A Survey of Techniques for Fulfilling the Time-Bound Requirements of Time-Sensitive IoT ApplicationsACM Computing Surveys10.1145/351041154:11s(1-36)Online publication date: 9-Sep-2022
  • Show More Cited By

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