Explainable AI for Root Cause Analysis in Large-Scale Datacenter Networks

Explainable AI for Root Cause Analysis in Large-Scale Datacenter Networks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 March 2026 V1 Latest version Share on Explainable AI for Root Cause Analysis in Large-Scale Datacenter Networks Author : Dheeraj Ramasahayam 0009-0001-5369-5606 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177490729.94263354/v1 77 views 36 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Black-box RCA models are difficult to trust in datacenter operations. This paper presents a real-data-driven explainable RCA framework that combines topology-aware temporal attention with operator-facing explanations. The benchmark is built from public CAIDA passive 100G statistics and MAWI samplepoint-F summaries, yielding 162 captures from January 1, 2024 through March 25, 2026. Because public traces do not expose switch-level RCA labels, controlled localized incidents are injected on top of real traffic windows in a Clos topology. On a 9-node benchmark with 500 windows of length 6, the temporal XAI model achieves 100% failure accuracy and 100% root-cause accuracy, matches Random Forest and LSTM baselines, outperforms a non-attention GNN on failure detection, and concentrates 76.1% of explanation mass in the top three nodes. Estimated operator RCA time drops from 10.15 to 5.33 minutes. Across 9-, 52-, and 104-node fabrics, the model maintains 100% failure and root-cause accuracy while explanation compactness declines from 0.66 to 0.15, motivating hierarchical pod-and rack-level aggregation. Supplementary Material File (tnsm_paper.pdf) Download 441.61 KB Information & Authors Information Version history V1 Version 1 30 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords datacenter networks explainable ai network management root cause analysis temporal attention Authors Affiliations Dheeraj Ramasahayam 0009-0001-5369-5606 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 77 views 36 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dheeraj Ramasahayam. Explainable AI for Root Cause Analysis in Large-Scale Datacenter Networks. Authorea . 30 March 2026. DOI: https://doi.org/10.22541/au.177490729.94263354/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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