Abstract
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× in insert throughput, 3.6× in write throughput, and 67% in query latency with lower memory consumption.
Highlights
INTRODUCTIONInternet of Things (IoT) applications, commonly deploying a myriad of sensors to collect a large amount of time-series data from physical environments around human beings, are designed to help us monitor [22], [31], analyze [10], [34], forecast our concerned events [14], [28], and make timely and correct responses
The associate editor coordinating the review of this manuscript and approving it for publication was Shaojun Wang. This necessitates Internet of Things (IoT) infrastructures to embrace edge computing that locally processes the data on edge nodes efficiently, in order to improve the responsiveness and prevent sensor-generated data from flooding the centralized cloud
2) We present Time Merged Tree to merge multiple tablets into a large group to flush to the storage devices with a single write operation, to improve the write throughput and to speed up inter-tablet join query operations
Summary
Internet of Things (IoT) applications, commonly deploying a myriad of sensors to collect a large amount of time-series data from physical environments around human beings, are designed to help us monitor [22], [31], analyze [10], [34], forecast our concerned events [14], [28], and make timely and correct responses. Existing time-series databases [2], [12], [22] separately ingest, organize, store, and query data of each stream in a rational table They provide standard inter-table operations to enable inter-stream data retrieval and processing, but at very high resource cost. We present EdgeDB, an efficient time-series database with edge servers (shown in Figure 1b) designed to manage thousands of high-sampling-frequency sensors while providing higher insert performance, query performance, and write speed, with lower resource requirement than existing databases. The contributions of this paper are as follows: 1) We propose a multi-stream merging mechanism to compactly organize multiple correlated streams together at runtime, supporting highly efficient insertion and join query operations; and introduce Time Partitioned Elastic Index to accelerate time-range queries with small memory overheads.
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