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Massive time-series data streams from high-sampling-frequency sensors in Internet of Things (IoT) can overwhelm the networks connecting the sensors to centralized clouds. Thus, edge computing servers have to be introduced to locally store and analyze growing time-series data. Unfortunately, conventional time-series databases exhibit low efficiency on edge nodes with limited resources for both computation and storage. In this paper, we propose a highly efficient time-series database, called EdgeDB, to fully utilize the capacity of the edge nodes. EdgeDB effectively improves the performances of both inserting and retrieving data from ingest streams by efficiently merging multiple streams and optimizing the storage data structure concurrently. EdgeDB first compactly organizes multiple online streams into a tablet within a time window and embeds predefined aggregate query results together. EdgeDB adopts Time Partitioned Elastic Index (TPEI) to build indexing on all tablets, enhancing the time-range query performance while reducing the memory usage by optimizing the indexing storage. EdgeDB further develops Time Merged Tree (TMTree) to combine a set of tablets into a large one, significantly boosting the write throughput and potentially strengthening the performance of inter-tablet query. Extensive experiments based on real-world datasets show that, compared with the state-of-the-art time-series database BTrDB, EdgeDB achieves performance improvements of up to $2.2\times $ in insert throughput, $3.6\times $ in write throughput, and 67% in query latency with lower memory consumption.

EdgeDB: An Efficient Time-Series Database for Edge Computing