# Power-Aware Scheduling for Multi-Center HPC Electricity Cost Optimization

arXiv: https://arxiv.org/abs/2503.11011 · HTML: https://mlsanity.com/paper/2503.11011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [A Practical Two-Stage Framework for GPU Resource and Power Prediction in Heterogeneous HPC Systems](https://mlsanity.com/paper/2604.02158/) (2026)

## Abstract (arXiv)

This paper introduces TARDIS (Temporal Allocation for Resource Distribution using Intelligent Scheduling), a novel power-aware job scheduler for High-Performance Computing (HPC) systems that minimizes electricity costs through both temporal and spatial optimization. Our approach addresses the growing concerns of energy consumption in HPC centers, where electricity expenses constitute a substantial portion of operational costs and have a significant financial impact. TARDIS employs a Graph Neural Network (GNN) to accurately predict individual job power consumption, then uses these predictions to strategically schedule jobs across multiple HPC facilities based on time-varying electricity prices. The system integrates both temporal scheduling, shifting power-intensive workloads to off-peak hours, and spatial scheduling, distributing jobs across geographically dispersed centers with different pricing schemes. We evaluate TARDIS using trace-based simulations from real HPC workloads, demonstrating cost reductions of up to 18% in temporal optimization scenarios and 10 to 20% in multi-site environments compared to state-of-the-art scheduling approaches, while maintaining comparable system performance and job throughput. Our comprehensive evaluation shows that TARDIS effectively addresses limitations in existing power-aware scheduling approaches by combining accurate power prediction with holistic spatial-temporal optimization, providing a scalable solution for sustainable and cost-efficient HPC operations.