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Energy-aware scheduling, carbon-conscious placement, GPU optimization, and workload consolidation. Enforced by ACIE causal inference and constraint solvers.
Schedule jobs to minimize energy consumption while meeting SLO requirements.
Place workloads to minimize carbon footprint based on grid carbon intensity.
Optimize GPU power caps to balance performance and energy efficiency.
Identify opportunities to consolidate workloads for improved energy efficiency.
Optimize training job energy by leveraging checkpoint/resume for demand response.
Optimize inference batch sizes for energy efficiency while meeting latency SLOs.
Optimize node hibernation strategies to reduce idle power consumption.
Analyze energy impact of multi-tenant workload isolation strategies.
Schedule preemptible workloads to maximize energy efficiency during low-demand periods.
Optimize memory tiering (DRAM/NVMe/CXL) for energy efficiency.
Place workloads considering network topology and energy costs.
Optimize storage I/O patterns for energy efficiency.
Design autoscaling policies that consider energy efficiency.
Leverage spot/preemptible instances for energy cost arbitrage.
Migrate workloads across clusters to optimize energy consumption.