Evidence-Constrained Incident Visualization Cards for Distributed Cloud Logs: A UI/UX Framework for Turning Hadoop, OpenStack, and ZooKeeper Logs into Actionable SRE Design Interfaces

Site reliability engineers do not need another opaque anomaly detector as much as they need interfaces that turn noisy distributed-system logs into fast, defensible incident decisions. This paper presents a reproducible UI/UX framework for LLM-assisted incident visualization cards. The framework converts raw and structured Loghub-2.0 benchmark logs from Hadoop, OpenStack, and ZooKeeper into evidence-grounded cards containing an incident headline, affected service and node, anomaly timeline, log-evidence badges, suspected root cause, confidence, recommended next action, severity hierarchy, and operator decision buttons. The empirical evaluation was conducted on the complete 2,000-line benchmark slice for each selected system, giving 6,000 log lines, 207 unique templates, and 120 fixed 50-line analysis windows. We implemented deterministic parsing, TF-IDF clustering, template-frequency z-score scoring, Isolation Forest, Local Outlier Factor, TF-IDF KMeans distance scoring, and a constrained LLM-style card generator. Drain-lite normalization achieved purity of 0.999 on Hadoop, 1.000 on OpenStack, and 1.000 on ZooKeeper against the Loghub EventId reference. The hybrid risk ensemble achieved Precision@5 of 0.800 on Hadoop, 0.800 on OpenStack, and 0.600 on ZooKeeper; the template-frequency z-score was the strongest single risk model on the ZooKeeper logs with AUPRC 0.934. The generated card interface increased the deterministic decision-readiness score from 0.133 for a raw log list and 0.486 for a plain-text summary to 0.944 for the visual incident card. These results show that evidence-preserving visual organization can improve the operational usefulness of log analytics outputs without claiming to replace human incident ownership.