Time-series databases for edge computing face unique challenges: cloud-based solutions are prohibitively expensive and latency-sensitive, while traditional distributed approaches don't support the temporal queries required by real-time applications like power grid monitoring. This work provides a novel key structure that enables efficient time-indexed access patterns in distributed edge storage systems.
This paper proposes a distributed hash table (DHT) based approach for storing and accessing time-series data at the edge of computing infrastructure. The work introduces time-factored DHT keys that enable efficient time-indexed reads and writes while maintaining distributed storage properties suitable for edge computing applications.
The paper presents two DHT key formats (Quanta-First and Key-First IDs) and evaluates their performance for time-indexed access. Results show that time-factored keys enable efficient localized data retrieval with low latency, supporting practical edge computing applications. The approach is demonstrated using a DHT implementation in Go deployed on Raspberry Pi clusters.
@inproceedings{Krentz2019,
author = {Krentz, Timothy and Dubey, Abhishek and Karsai, Gabor},
booktitle = {IEEE} 22nd International Symposium on Real-Time Distributed Computing, {ISORC} 2019, Valencia, Spain, May 7-9, 2019},
title = {Short Paper: Towards An Edge-Located Time-Series Database},
year = {2019},
pages = {151--154},
abstract = {Smart infrastructure demands resilient data storage, and emerging applications execute queries on this data over time. Typically, time-series databases serve these queries; however, cloud-based time-series storage can be prohibitively expensive. As smart devices proliferate, the amount of computing power and memory available in our connected infrastructure provides the opportunity to move resilient time-series data storage and analytics to the edge. This paper proposes time-series storage in a Distributed Hash Table (DHT), and a novel key-generation technique that provides time-indexed reads and writes for key-value pairs. Experimental results show this technique meets demands for smart infrastructure situations.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/isorc/KrentzDK19},
category = {selectiveconference},
contribution = {minor},
doi = {10.1109/ISORC.2019.00037},
file = {:Krentz2019-Towards_An_Edge-Located_Time-Series_Database.pdf:PDF},
keywords = {time-series database, distributed hash table, edge computing, smart grid, key-value storage},
project = {cps-middleware},
tag = {platform},
timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
url = {https://doi.org/10.1109/ISORC.2019.00037}
}