TRIAD: DEADLINE- AND RELIABILITY-AWARE SCHEDULING FOR POST-EARTHQUAKE STRUCTURAL ASSESSMENT WORKFLOWS
Keywords:
Urgent computing, Workflow scheduling, Deadline, Reliability, Overload management, Regional earthquake loss assessment, HazusAbstract
Post-earthquake structural assessment requires timely execution of computational workflows under rapidly changing workloads, strict deadlines, and uncertain computing resource availability. Conventional scheduling approaches often prioritize deadlines without adequately accounting for overload and execution failures, potentially delaying high-priority assessments. This paper presents TRIAD (Triage with Reliability-Inflated Admission and Dispatch), an online scheduling framework that integrates failure-aware workload estimation, probabilistic deadline assessment, priority-based admission control, and reliability-aware task dispatch. TRIAD dynamically estimates worker failure conditions, adjusts expected execution requirements, and prioritizes assessment workflows according to their operational urgency and probability of timely completion. We evaluate TRIAD using trace-driven simulations constructed from building inventories in San Mateo and Alameda, California, a USGS earthquake scenario, FEMA Hazus v6.1 damage-and-loss information, and execution measurements from 446 OpenSees structural analyses. Workload arrivals, deadline constraints, and computing resource failures are simulated to examine scheduling performance under post-earthquake operating conditions. Compared with eight baseline scheduling methods, TRIAD achieves a weighted deadline satisfaction ratio of 0.875 on the San Mateo workload, exceeding the strongest baseline at 0.860 and conventional earliest-deadline-first scheduling at 0.260. It also reduces the weighted commitment-violation rate from 0.112 to 0.093 while maintaining sub-millisecond scheduling overhead. The results demonstrate the value of combining reliability estimation and overload-aware admission control for improving the timeliness and dependability of computational structural assessment workflows.References
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