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Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

Journal Article · · Journal of Computing and Information Science in Engineering
DOI:https://doi.org/10.1115/1.4055733· OSTI ID:2418608
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Abstract

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. By reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

Research Organization:
Georgia Institute of Technology, Atlanta, GA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
DOE Contract Number:
EE0008303
OSTI ID:
2418608
Journal Information:
Journal of Computing and Information Science in Engineering, Journal Name: Journal of Computing and Information Science in Engineering Journal Issue: 6 Vol. 22; ISSN 1530-9827
Publisher:
ASME
Country of Publication:
United States
Language:
English

References (12)

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Cyber-physical production systems: roots from manufacturing science and technology journal October 2015
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A Review of Cyber-Physical System Research Relevant to the Emerging IT Trends: Industry 4.0, IoT, Big Data, and Cloud Computing journal September 2017
Cloud computing journal July 2008
An Internet of Things for Manufacturing (IoTfM) Enterprise Software Architecture journal February 2018
Scalable SQL and NoSQL data stores journal May 2011
An overview of data warehousing and OLAP technology journal March 1997
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems journal January 2015
Advanced Message Queuing Protocol journal November 2006
Experiences creating a framework for smart traffic control using AWS IOT conference December 2016

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Related Subjects

Computer Science
Engineering