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Data-Driven Network Operations: Architecture for Telemetry, Analytics, and Automated Remediation

Manevannan Ramasamy

Software Engineering Senior Technical Leader, Milpitas, California, United States of America

Abstract

The transition to data-driven decision-making, particularly when it comes to contemporary network operations, can not be achieved without an efficient form of automation, yet to approach it, it is required that more than high-volume data collection by telemetry is involved. This paper outlines a design of reliable network operations that includes telemetry ingestion, data quality management, temporal modeling and topology awareness, analytics and automated remediation as a part of a controlled data lifecycle. The architecture suggests a canonical event envelope to offer consistent identity, event time, semantics and provenance to heterogeneous sources. Topology model and temporal entity assist in giving operational context and purpose-specific data store and controlled transformation pipeline are useful in assisting assurance, capacity planning, anomaly detection and remediation processes. A feature registry makes reusable analytical representations and the evidence provenance keeps track of the provenance between the source observations, transformations, analytical outputs and operational actions. The framework specifically addresses the problem of incomplete, late, repetitive, and semantically incoherent information by incorporating measures of quality and decision processes that are conscious of the confidence. Framework assessment is a framework appraisal that gauges completeness, freshness, semantic consistency, lineage coverage, reproducibility, and downstream quality of choice. Based on the proposed architecture, credible automated remediation is achievable, as indicated by the capabilities to reuse the evidence of all decisions to improve the transparency, operational soundness and regulated reuse of network data throughout the transforming analytic and automation processes

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 8 No. 3 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2023.0803002

Pages

13601-13611

Published

May 15, 2023
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Manevannan Ramasamy (%2023). Data-Driven Network Operations: Architecture for Telemetry, Analytics, and Automated Remediation. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 3 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 13601-13611. https://doi.org/10.15662/ijetemr.2023.0803002

References

[1] H. Song et al., “Network telemetry framework,” RFC 9232, May 2022, doi: 10.17487/RFC9232.
[2] W3C, “PROV-O: The PROV ontology,” W3C Recommendation, Apr. 2013. [Online]. Available: W3C PROV-O Specification
[3] OpenLineage Project, “OpenLineage specification.” [Online]. Available: OpenLineage Specification
[4] OpenConfig, “gRPC Network Management Interface (gNMI) specification.” [Online]. Available: OpenConfig gNMI Specification
[5] M. Björklund et al., “Network management datastore architecture,” RFC 8342, Mar. 2018, doi: 10.17487/RFC8342.
[6] D. Bhamare, A. Kassler, J. Vestin, M. A. Khoshkholghi, J. Taheri, T. Mahmoodi, P. Öhlén, and C. Curescu, “IntOpt: In-band network telemetry optimization framework to monitor network slices using P4,” Computer Networks, vol. 216, Art. no. 109214, 2022, doi: 10.1016/j.comnet.2022.109214.
[7] G. Tangari, D. Tuncer, M. Charalambides, Y. Qi, and G. Pavlou, “Self-adaptive decentralized monitoring in software-defined networks,” IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1277–1291, 2018.
[8] D. Ding, M. Savi, G. Antichi, and D. Siracusa, “An incrementally-deployable P4-enabled architecture for network-wide heavy-hitter detection,” IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 75–88, 2020.
[9] S. Tang, D. Li, B. Niu, J. Peng, and Z. Zhu, “Sel-INT: A runtime-programmable selective in-band network telemetry system,” IEEE Transactions on Network and Service Management, 2019.
[10] A. G. Prieto and R. Stadler, “A-GAP: An adaptive protocol for continuous network monitoring with accuracy objectives,” IEEE Transactions on Network and Service Management, vol. 4, no. 1, pp. 2–12, 2007.
[11] S. Lee, K. Levanti, and H. S. Kim, “Network monitoring: Present and future,” Computer Networks, vol. 65, pp. 84–98, 2014.
[12] R. Jain and S. Paul, “Network virtualization and software defined networking for cloud computing: A survey,” IEEE Communications Magazine, vol. 51, no. 11, pp. 24–31, 2013.
[13] M. Yu, L. Jose, and R. Miao, “Software defined traffic measurement with OpenSketch,” in Proc. 10th USENIX Symp. Networked Systems Design and Implementation (NSDI), 2013.
[14] A. Gupta, R. Harrison, A. Pawar, R. Birkner, M. Canini, N. Feamster, J. Rexford, and W. Willinger, “Sonata: Query-driven network telemetry,” arXiv preprint arXiv:1706.08030, 2017.