IntDB: Towards a Spatiotemporal Database for In-band Network Telemetry

In-band Network Telemetry (INT) provides finegrained, path-level network visibility by embedding measurement data into live packets. However, the nested, spatiotemporal nature of INT data poses significant challenges for traditional telemetry storage systems, which are primarily optimized for time series data. In this paper, we present IntDB, a spatiotemporal database for INT. IntDB introduces a path-oriented data model that preserves end-to-end flow structure and supports efficient multihop correlation and querying. Experimental results show that IntDB outperforms existing solution in both writing scalability and query performance, making it a practical foundation for INT-based network measurement.

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Publication Details

Published
2025-09-22
DOI
https://doi.org/10.23919/apnoms67058.2025.11181327
Primary Topic
Data Mining Algorithms and Applications
Type
article
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article

IntDB: Towards a Spatiotemporal Database for In-band Network Telemetry

Xin Luna Dong, Lizhuang Tan, James Won‐Ki Hong, Nguyen Van Tu
Data Mining Algorithms and Applications
article

IntDB: Towards a Spatiotemporal Database for In-band Network Telemetry

Xin Luna Dong, Lizhuang Tan, James Won‐Ki Hong, Nguyen Van Tu
article en

Abstract

In-band Network Telemetry (INT) provides finegrained, path-level network visibility by embedding measurement data into live packets. However, the nested, spatiotemporal nature of INT data poses significant challenges for traditional telemetry storage systems, which are primarily optimized for time series data. In this paper, we present IntDB, a spatiotemporal database for INT. IntDB introduces a path-oriented data model that preserves end-to-end flow structure and supports efficient multihop correlation and querying. Experimental results show that IntDB outperforms existing solution in both writing scalability and query performance, making it a practical foundation for INT-based network measurement.

Pohang University of Science and Technology (KR), Qilu University of Technology (CN)
Openalex Percentile: Top 59%
Data Mining Algorithms and Applications
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