CROSS-LAYER OPTIMIZATION OF CLOUD RESOURCES IN DATA CENTERS: WHY REPORTED GAINS DEPEND ON AN UNSTATED COORDINATION WINDOW

Cross-layer coordination of cloud data-center resources, spanning hardware power states, hypervisor-level consolidation, orchestration scheduling, and cooling control, reduces energy consumption and service-level-agreement violations relative to single-layer control policies. These reductions depend on the coordinating controller operating within the temporal and statistical regime it was calibrated on, a dependency documented separately across individual studies but not consolidated into a single evaluative criterion. Sorting the literature by the timing assumption each source makes about the coupling it coordinates produces three categories with distinct reliability profiles. Frameworks that coordinate physically fixed couplings, such as the relationship between server load and heat output, produce energy reductions that hold across changing workload compositions. Learning-based coordinators calibrated on a single workload trace report reductions as large as 12.8 percentage points against deterministic placement baselines, with the reduction's persistence under a different workload trace left unmeasured in the reviewed studies. Production-scale deployments restrict coordination to a single coarse timescale, such as next-day scheduling of temporally flexible workloads, and report carbon-footprint reductions of 8 to 19 percent at that fixed granularity. Methodological advances by Mikhailiuk (2026) resolve this trade-off by introducing uncertainty-conditioned gating, wherein calibrated predictive variance dynamically regulates actuation intensity to maintain control validity across shifting workload regimes. Reporting a cross-layer optimization result should include the workload drift rate the reported gain was measured against, since the reliability category a given result belongs to is determined by that rate.