一种基于规则与AI增强的混合框架，用于DevOps部署中的自动故障恢复
A Hybrid Rule-Based and AI-Augmented Framework for Automatic Failure Recovery in DevOps Deployments
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中文总结 AI 辅助
该研究提出一种结合规则与AI预测的混合框架，用于DevOps部署中自动故障恢复，通过决策树等模型实现高准确率，显著降低恢复时间和停机成本。
中文摘要 AI 辅助
自动故障恢复是DevOps部署中的另一个难点，原因在于系统复杂性、高工作负载以及传统基于规则或手动方法的局限性。本研究提出了一种混合模型，将确定性的基于规则的恢复与机器学习辅助的故障预测相结合，以自动监控、识别、分类并实时恢复故障。为测试该架构，使用了包含100,000条记录的分布式日志数据集，用于决策树、随机森林、逻辑回归、LightGBM、Autoencoder与BiLSTM等模型。决策树在缺陷识别方面表现强劲，F1分数为80.8%，召回率为80.0%，精确率为81.6%，准确率为89.4%。自动恢复措施实现了令人瞩目的83.3%成功率，导致平均恢复时间（MTTR）减少94.6%，停机时间减少95.4%，并每年节省7,462单位成本。该框架结合了可解释的基于规则的逻辑和自适应的AI预测，使其更具韧性、运营效率更高且可扩展，在受控实验条件下评估，为DevOps环境提供了一种有效的自主方法。
英文摘要
Automatic failure recovery is another difficult area of DevOps deployments due to the complexity of the system, high workload and the limitations of the traditional rules-based or manual approach. This study proposes a hybrid model integrating deterministic, rule-based recovery with the assistance of machine learning by fault prediction to automatically monitor failures, identify, classify and recover failures in real time. To test the architecture, a distributed log dataset of 100,000 records was used for models such as Decision Tree, Random Forest, Logistic Regression, LightGBM, Autoencoder with BiLSTM. The Decision Tree had a strong performance in defect identification with an F1 score of 80.8%, recall of 80.0%, precision of 81.6%, and accuracy of 89.4%. An impressive 83.3% success rate was achieved by the automated recovery measures, resulting in a 94.6% decrease in mean time to recovery (MTTR), a 95.4% reduction in downtime, and annual savings of 7,462. The combination of explainable rule-based logic and adaptive AI prediction in the framework means it is more resilient, operationally efficient, and scalable, and an effective, autonomous approach to DevOps environments evaluated under controlled experimental conditions.
