" Empirical Resilience Metrics and Failure Patterns in Mission-Critical Financial Mainframe Infrastructures"

"Abstract:This dataset provides a comprehensive longitudinal technical analysis of performance metrics and resilience patterns within mission-critical IBM Mainframe (z\/OS) and high-end Storage (IBM 2105 Shark) ecosystems. Drawing from over 20 years of specialized L3 (Subject Matter Expert) interventions within the Brazilian National Financial System, the data categorizes critical hardware and microcode failure modes, Mean Time To Repair (MTTR) benchmarks, and the operational effectiveness of high-level recovery protocols.The objective of this dataset is to establish a technical framework for infrastructure engineers and disaster recovery specialists to maintain 99.999% availability in large-scale financial environments. By providing empirical data on incident management and systemic stabilization, this contribution serves as a vital resource for mitigating systemic risks in global banking architectures and advancing the standards of critical infrastructure governance. "

摘要：本数据集针对关键任务级IBM大型主机（z/OS操作系统）与高端存储设备（IBM 2105 Shark）生态系统内的性能指标及韧性模式，开展全面的纵向技术分析。数据集基于巴西国家金融系统内二十余年的三级（主题专家）专项技术干预数据，对关键硬件与微代码故障模式、平均修复时间（MTTR）基准值，以及高层级恢复协议的运营效能进行了分类整理。本数据集的目标是为基础设施工程师与灾难恢复专家构建技术框架，以保障大规模金融环境实现99.999%的可用性。通过提供事件管理与系统稳定化相关的实证数据，本数据集可为全球银行架构的系统性风险缓解提供重要资源支撑，并助力推动关键基础设施治理标准的升级。