Selective Admission and Occupancy-Targeted Placement for Carbon-Aware Kubernetes Scheduling: A Measurement- Calibrated CERN Case Study

Temporal carbon-aware scheduling can reduce electricity-related emissions, but deferring work to lower carbon-intensity intervals can concentrate demand and increase waiting. We present benefit-gated deferral (BGD), a selective admission policy for nonpreemptive batch jobs on shared clusters, evaluated using realistic high-energy physics workloads in a measurement-calibrated CERN case study. BGD requires relative and absolute estimated carbon gains and ranks eligible starts by estimated grams avoided per hour waited. Arrival patterns extracted from CERN’s Next Generation Triggers platform define the primary scenario. Measurements of CMS simulation and reconstruction, ATLAS event generation, and LHCb simulation calibrate occupancy-dependent power and runtime. A 100-worker simulation compares BGD with immediate admission under simulated default Kubernetes and occupancy-targeted placement over a 90-day French carbon-intensity series. Synthetic arrivals provide a controlled sensitivity benchmark. With 24 h flexibility, the simulations yield accounted-emissions reductions of 17.40% and 7.98% under default placement at 30% and 50% offered load. Adding occupancy-targeted placement increases these reductions to 28.34% and 12.17%, while 95th-percentile waits exceed 20 h. Completion flexibility and occupancy-dependent execution affect the benefit of deferral, which must be assessed alongside waiting times and deadline compliance.