The Embedded IoT Time Series Database for Hybrid Solid-State Storage System

IoT time series data is an important form of big data. How to improve the efficiency of storage system is crucial for IoT time series database to store and manage massive IoT time series data from various IoT devices. Mixing NVM and SSD is an effective method to improve the I/O performance of storag...

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Autores principales:	Tao Cai, Peiyao Liu, Dejiao Niu, Jiancong Shi, Lei Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/af25733de377444391199c91b5048fbe
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spelling	oai:doaj.org-article:af25733de377444391199c91b5048fbe2021-11-08T02:37:13ZThe Embedded IoT Time Series Database for Hybrid Solid-State Storage System1875-919X10.1155/2021/9948533https://doaj.org/article/af25733de377444391199c91b5048fbe2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9948533https://doaj.org/toc/1875-919XIoT time series data is an important form of big data. How to improve the efficiency of storage system is crucial for IoT time series database to store and manage massive IoT time series data from various IoT devices. Mixing NVM and SSD is an effective method to improve the I/O performance of storage systems. However, there are great differences between HDD and NVM or SSD. As a result, NVM and SSD cannot be directly used in the current time series database effectively. We design an IoT time series database with an embedded engine in storage device drivers for the hybrid solid-state storage system consisting of NVM and SSD. The I/O software stack of storing and managing IoT time series data can be shortened to improve the efficiency. Based upon the intrinsic characteristics of IoT time series data and different features of NVM and SSD, a redundancy elimination and compression fusion strategy, a hierarchical management strategy, and a heterogeneous time series data index are designed to improve the efficiency. Finally, a prototype of embedded IoT time series database named TS-NSM is implemented, and YCSB-TS is used to measure the IOPS. The results show that TS-NSM can improve the write IOPS up to 243.6 times and 174.3 times, respectively, compared with InfluxDB and OpenTSDB, and improve the read IOPS up to 10.1 times and 14.4 times, respectively.Tao CaiPeiyao LiuDejiao NiuJiancong ShiLei LiHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Tao Cai Peiyao Liu Dejiao Niu Jiancong Shi Lei Li The Embedded IoT Time Series Database for Hybrid Solid-State Storage System
description	IoT time series data is an important form of big data. How to improve the efficiency of storage system is crucial for IoT time series database to store and manage massive IoT time series data from various IoT devices. Mixing NVM and SSD is an effective method to improve the I/O performance of storage systems. However, there are great differences between HDD and NVM or SSD. As a result, NVM and SSD cannot be directly used in the current time series database effectively. We design an IoT time series database with an embedded engine in storage device drivers for the hybrid solid-state storage system consisting of NVM and SSD. The I/O software stack of storing and managing IoT time series data can be shortened to improve the efficiency. Based upon the intrinsic characteristics of IoT time series data and different features of NVM and SSD, a redundancy elimination and compression fusion strategy, a hierarchical management strategy, and a heterogeneous time series data index are designed to improve the efficiency. Finally, a prototype of embedded IoT time series database named TS-NSM is implemented, and YCSB-TS is used to measure the IOPS. The results show that TS-NSM can improve the write IOPS up to 243.6 times and 174.3 times, respectively, compared with InfluxDB and OpenTSDB, and improve the read IOPS up to 10.1 times and 14.4 times, respectively.
format	article
author	Tao Cai Peiyao Liu Dejiao Niu Jiancong Shi Lei Li
author_facet	Tao Cai Peiyao Liu Dejiao Niu Jiancong Shi Lei Li
author_sort	Tao Cai
title	The Embedded IoT Time Series Database for Hybrid Solid-State Storage System
title_short	The Embedded IoT Time Series Database for Hybrid Solid-State Storage System
title_full	The Embedded IoT Time Series Database for Hybrid Solid-State Storage System
title_fullStr	The Embedded IoT Time Series Database for Hybrid Solid-State Storage System
title_full_unstemmed	The Embedded IoT Time Series Database for Hybrid Solid-State Storage System
title_sort	embedded iot time series database for hybrid solid-state storage system
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/af25733de377444391199c91b5048fbe
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The Embedded IoT Time Series Database for Hybrid Solid-State Storage System

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