Autonomous Self-Healing Datacenter Networks: A Unified AI System for Prediction, Detection, Root Cause Analysis, and Recovery

Autonomous Self-Healing Datacenter Networks: A Unified AI System for Prediction, Detection, Root Cause Analysis, and Recovery | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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 Autonomous Self-Healing Datacenter Networks: A Unified AI System for Prediction, Detection, Root Cause Analysis, and Recovery Author : Dheeraj Ramasahayam 0009-0001-5369-5606 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177402935.53246540/v1 114 views 69 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Modern datacenter networks still operate through fragmented workflows in which predictive maintenance, intrusion detection, root cause analysis (RCA), and remediation are studied separately and deployed through loosely coupled tooling. This paper presents a unified AI system for autonomous self-healing datacenter networks that connects four stages: temporal failure prediction, drift-adaptive intrusion detection, topology-aware RCA, and safety-gated recovery with counterfactual validation. The architecture combines streaming telemetry, network-flow analytics, graph reasoning, and a topology digital twin inside a single operational loop. The system is formalized as a constrained sequential decision problem over telemetry, flows, topology, and policy constraints, and is evaluated through staged module validation plus a trace-driven closed-loop emulation. Because no public benchmark spans all four stages jointly, the empirical evidence combines public telemetry and flow datasets, streaming emulation, packet-capture replay, topology-grounded recovery traces, and a synthetic end-to-end incident timeline that makes the module handoff contract explicit. Across failure-prediction benchmarks, the temporal sequence model reaches F1 scores of 0.3737 on optical zero-shot hard-failure evaluation and 0.4677 on Cisco BGP failure prediction within a 60-second warning window. In intrusion detection, the drift-adaptive hybrid improves weighted F1 from 61.35% to 68.69% on full CICIDS2017 cross-dataset transfer without retraining the base detectors and reaches 98.05% weighted F1 in a packet-capture replay case study. For RCA, topology-aware reasoning reaches 0.8380 target-localization F1 with 1.0000 hidden-target accuracy and 0.9394 temporal RCA accuracy at 5.2 s mean detection delay. In the recovery twin, gated actions improve mean reachability from 0.9740 to 1.0000, achieve 0.8182 recovery success, and block 100% of mismatched unsafe actions. The results show that prediction, detection, diagnosis, and remediation can be organized into a reproducible closed loop for next-generation self-healing datacenter networks. Supplementary Material File (paper.pdf) Download 98.46 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 concept drift datacenter networks digital twins failure prediction graph neural networks intrusion detection root cause analysis self-healing systems Authors Affiliations Dheeraj Ramasahayam 0009-0001-5369-5606 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 114 views 69 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dheeraj Ramasahayam. Autonomous Self-Healing Datacenter Networks: A Unified AI System for Prediction, Detection, Root Cause Analysis, and Recovery. Authorea . 20 March 2026. DOI: https://doi.org/10.22541/au.177402935.53246540/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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