A New IoT Storage System Based on Raw NVM
Tao Cai, Yueming Ma, Dejiao Niu, Pengfei Gao, Tianle Lei, Jianfei Dai · 2022 IEEE International Conference on Big Data (Big Data) · 2022
NVM storage devices have the advantages of high read-write speed, non-volatile and large capacity, which provides support for efficient storage and management of a large number of time series data collected by IoT devices. However, how to study a new IoT storage system for time series data according to the advantages of NVM storage devices is an important issue that needs to be solved. This paper first analyses the characteristics of accessing to IoT time series data and then based on the characteristics of NVM storage devices, a multi-granularity auto-converting structure for time series data is designed. It not only reflects the timeliness of accessing to IoT time series data, but also avoids additional data replication, which improves the storage efficiency of IoT time series data. A timeliness-based heterogeneous query strategy is designed to improve the query efficiency according to the IoT time series data storage structure and accessing characteristics. The prototype of a new IoT storage system based on raw NVM named NBTSMS is implemented based on the Intel open-source NVM storage device driver PMEM. InfluxDB, OpenTSDB, and TimescaleDB are used for evaluation with YCSB-TS. Results show that NSTSMS can improve write throughput by 137%, random query throughput by 153.7%, scan throughput by 189.4%, and mixed-operations throughput by 55%.