Neural Symbolic Cloud Healing Verifies Kubernetes Recovery Plans with Formal Verification

Authors

  • Miguel Ramos
  • Kanna Matsuda
  • Hector Campos

Keywords:

Cloud Computing, Kubernetes, Neural-Symbolic AI, Automated Healing, Formal Verification

Abstract

The rapid proliferation of containerized microservices has positioned Kubernetes as the dominant orchestration platform in modern cloud computing. However, the inherent complexity and dynamic nature of large-scale Kubernetes clusters present unprecedented challenges for automated fault recovery and system healing. Traditional rule-based recovery systems lack the adaptability required for complex, unforeseen anomalies, while pure machine learning approaches suffer from a lack of interpretability and cannot provide formal guarantees regarding the safety of their generated recovery plans. To address this critical gap, this paper proposes a comprehensive neural-symbolic cloud healing framework specifically designed for Kubernetes recovery plan verification tasks. By synergistically combining the pattern recognition capabilities of neural networks with the rigorous formal logic of symbolic reasoning, the proposed architecture generates adaptive recovery strategies and mathematically verifies their safety prior to execution. The neural component leverages deep learning to analyze high-dimensional telemetry data and predict optimal healing actions, whereas the symbolic component employs a deterministic rule engine to verify that these actions adhere to strict operational constraints and do not induce cascading failures. Through extensive analysis and simulation, this study demonstrates that the neural-symbolic approach significantly reduces the deployment of erroneous recovery plans while maintaining high system availability. The findings provide a robust theoretical foundation and practical blueprint for developing next-generation, self-healing cloud infrastructures that prioritize both autonomy and verifiable reliability.

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Published

2026-06-10

Issue

Vol. 4 No. 01 (2026): International Journal of Innovative Research

Section

Articles

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Copyright (c) 2026 Miguel Ramos, Kanna Matsuda, Hector Campos

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