S4eS: a lower-bound complexity scheduling algorithm for energy savings on DVFS-enabled platforms

The rapid expansion of artificial intelligence (AI) and cloud services has significantly increased data center energy demand, motivating efficient scheduling strategies on DVFS-enabled heterogeneous platforms. Within the Advanced Configuration and Power Interface (ACPI) framework, Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS) reduce static and dynamic energy consumption; however, applying them to dependent-task workflows under deadline constraints introduces complex trade-offs among static, frequency-independent, and frequency-dependent energy components. To address these challenges, this article presents Scheduling for Energy Saving ( S4eS ), a compile-time energy-aware scheduling algorithm with provably lower-bound time complexity. S4eS integrates a DPM module that selects compute nodes for switch-off using a theory-grounded criterion based on frequency-independent energy, and a DVFS module that determines task-level frequency scaling to maximize net energy savings while respecting deadlines. Theoretical analysis establishes the lower-bound complexity of both modules, ensuring scalability for large-scale environments. Experimental results on synthetic and real-world workflows show that S4eS achieves average energy savings of 20.08%, outperforming related methods while maintaining reduced computational overhead.