Browse > Article
http://dx.doi.org/10.5392/JKCA.2021.21.07.127

Design and Evaluation of a High-performance Key-value Storage for Industrial IoT Environments  

Han, Hyuck (동덕여자대학교 컴퓨터학과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.7, 2021 , pp. 127-133 More about this Journal
Abstract
In industrial IoT environments, sensors generate data for their detection targets and deliver the data to IoT gateways. Therefore, managing large amounts of real-time sensor data is an essential feature for IoT gateways, and key-value storage engines are widely used to manage these sensor data. However, key-value storage engines used in IoT gateways do not take into account the characteristics of sensor data generated in industrial IoT environments, and this limits the performance of key-value storage engines. In this paper, we optimize the key-value storage engine by utilizing the features of sensor data in industrial IoT environments. The proposed optimization technique is to analyze the key, which is the input of a key-value storage engine, for further indexing. This reduces excessive write amplification and improves performance. We implement our optimization scheme in LevelDB and use the workload of the TPCx-IoT benchmark to evaluate our proposed scheme. From experimental results we show that our proposed technique achieves up to 21 times better than the existing scheme, and this shows that the proposed technique can perform high-speed data ingestion in industrial IoT environments.
Keywords
Industrial IoT; Sensor Data; Key-Value Storage Engine; LSM Tree; Write Amplification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. Xie, B. Chandramouli, Y. Li, and D. Kossmann, "FishStore: Faster Ingestion with Subset Hashing," ACM SIGMOD, 2019.
2 E. Sisinni, A. Saifullah, S. Han, U. Jennehag, and M. Gidlund, "Industrial Internet of Things: Challenges, Opportunities, and Directions," IEEE Transactions on Industrial Informatics, Vol.14, No.11, 2018.
3 https://github.com/google/leveldb
4 M. Poess, R. Nambiar, K. Kulkarni, C. Narasimhadevara, T. Rabl, and H. Jacobsen, "Analysis of TPCx-IoT: The First Industry Standard Benchmark for IoT Gateway Systems," IEEE ICDE, 2018.
5 M. P. Andersen and D. E. Culler, "BTrDB: Optimizing Storage System Design for Timeseries Processing," USENIX FAST, 2016.
6 L. Lu, T. S. Pillai, H. Gopalakrishnan, A. C. Arpaci-Dusseau, and R. H. Arpaci-Dusseau, "Wisckey: Separating Keys from Values in SSD-Conscious Storage," ACM Transactions on Storage, Vol.13, No.1, 2017.
7 H. H. Chan, C. J. M. Liang, Y. Li, W. He, P. P. Lee, L. Zhu, Y. Dong,Y. Xu, Y. Xu, and J. Jianget al., "HashKV: Enabling Efficient Updates in KVStorage via Hashing," USENIX ATC, 2018.
8 R. Ramakrishnan and J. Gehrke, Database management systems, Osborne/McGraw- Hill, 2000.
9 Ryan Johnson, Ippokratis Pandis, Radu Stoica, Manos Athanassoulis, and Anastasia Ailamaki, "Aether: a scalable approach to logging," VLDB, 2010.
10 Paul E. McKenney, Joel Fernandes, Silas Boyd-Wickizer, and Jonathan Walpole, "RCU Usage In the Linux Kernel: Eighteen Years Later," SIGOPS Oper. Syst. Rev. Vol.54, 2020.
11 P. Helland, H. Sammer, J. Lyon, R. Carr, P. Garrett, and A. Reuter, "Group commit timers and high volume transaction systems," HPTS, 1987.
12 Patrick O'Neil, Edward Cheng, Dieter Gawlick, and Elizabeth O'Neil. "The log-structured merge-tree (LSM-tree)," Acta Inf., Vol.33, No.4, 1996.
13 B. Chandramouli, G. Prasaad, D. Kossmann, J. Levandoski, J. Hunter, and M. Barnett, "FASTER: A Concurrent Key-Value Store with In-Place Updates," ACM SIGMOD, 2018.
14 Y. Yang, Q. Cao, and H. Jiang, "EdgeDB: An Efficient Time-Series Database for Edge Computing," IEEE Access, Vol.7, 2019.
15 B. B. Brandenburg and J. H. Anderson, "Reader-Writer Synchronization for Shared-Memory Multiprocessor Real-Time Systems," 21st Euromicro Conference on Real-Time Systems, 2009.