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Workload-optimized sensor data store for industrial IoT gateways
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AI
DOI：10.1016/j.future.2022.05.012.png)
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摘要
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In industrial Internet of Things (IoT) environments, sensor devices continue to generate a stream of sensor data, and the management of an ever-growing amount of data is a vital feature for IoT gateways. However, our preliminary analysis of some popular key-value stores used in IoT gateways revealed that none of the systems exploits the distinctive characteristics of IoT workloads - append-only and immutable - thus the systems show limitations in managing sensor data. To address this issue, in this study, we propose Indexing-of-Indexes (IoI) and LogFlush-and-Append (LFA) to exploit such characteristics. IoI is an indexing and data organization scheme designed to eliminate the notorious compaction-induced write amplification observed in legacy key-value stores, and LFA is a data ingestion scheme intended to remove double data write issues in legacy write-ahead logging implementations. We implement a prototype key-value store, SEN-STORE, which incorporates our proposals, and we evaluate its performance using synthetic workloads and the TPCx-IoT benchmark. The evaluation results show that SEN-STORE achieves up to 17.6x and 2.1x higher IoTps than industry-leading RocksDB and state-of-the-art IoTDB, respectively. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Industrial IoT
IoT gateway
Key-value store
Sensor data store
Index structure
Write amplification
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6.1
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6.8K
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