ClosRCA-Bench: An Open Topology-Grounded Benchmark and Counterfactual Recovery Framework for Self-Healing Datacenter Networks

ClosRCA-Bench: An Open Topology-Grounded Benchmark and Counterfactual Recovery Framework for Self-Healing 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 20 March 2026 V1 Latest version Share on ClosRCA-Bench: An Open Topology-Grounded Benchmark and Counterfactual Recovery Framework for Self-Healing Datacenter Networks Author : Dheeraj Ramasahayam 0009-0001-5369-5606 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177402886.66308404/v1 100 views 56 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Open research on datacenter-network root cause analysis (RCA) is limited by two recurring problems: many studies rely on private production traces, and public studies often omit the topology and remediation context needed for self-healing systems. This paper introduces ClosRCA-Bench, a reproducible topology-grounded benchmark constructed from Cisco's public Clos-topology telemetry repository by combining event files, CDP maps, and per-device YANG telemetry into fixed graph windows. The resulting benchmark contains 311 windows over 11 topology nodes with 30 features per node and four cause families: BFD outage, blackhole, ECMP change, and interface shutdown. Two fault classes localize to hidden target devices that are not directly monitored, making topology-aware localization a first-class task. The paper evaluates rule-based, correlation-based, graph-only, and spatio-temporal graph RCA methods, then measures remediation with a safety gate and a counterfactual topology digital twin. On the held-out split, Random Forest achieves the strongest anomaly F1 at 0.9688 and weighted RCA cause F1 at 0.9707. The full STGNN reaches 0.8380 weighted F1 for target-device localization and 1.0000 hidden-target accuracy, while the no-topology ablation collapses to 0.0000 hidden-target accuracy. In temporal tracking, the full STGNN attains 0.9394 RCA accuracy with 5.2 s mean detection delay, improving over the graph-only baseline's 6.3 s delay at the same RCA accuracy. On the compound-failure slice, the full STGNN retains 0.9130 cause accuracy compared with 1.0000 on single-failure windows. In the counterfactual recovery twin, gated actions improve mean reachability from 0.9740 under fault to 1.0000 after recovery and achieve 0.8182 recovery-success rate while blocking 100% of mismatched unsafe actions. The main contribution is therefore an open benchmark and evaluation protocol that makes topology-aware, temporal, and recoveryaware RCA measurable. Supplementary Material File (closrca-bench-tnsm-main-document.pdf) Download 933.76 KB Information & Authors Information Version history V1 Version 1 20 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords anomaly detection benchmark datasets datacenter networks graph neural networks network telemetry root cause analysis self-healing systems Authors Affiliations Dheeraj Ramasahayam 0009-0001-5369-5606 [email protected] Independent Researcher United States View all articles by this author Metrics & Citations Metrics Article Usage 100 views 56 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dheeraj Ramasahayam. ClosRCA-Bench: An Open Topology-Grounded Benchmark and Counterfactual Recovery Framework for Self-Healing Datacenter Networks. Authorea . 20 March 2026. DOI: https://doi.org/10.22541/au.177402886.66308404/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. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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