Paper Detail
Tianyu Wang, Gourav Rattihalli, Aditya Dhakal, Longfei Shangguan, Dejan Milojicic
As LLM inference becomes a major cloud workload, its growing energy footprint makes cluster-wide energy optimization increasingly important. Serverless LLM serving helps platforms absorb traffic volatility by elastically sharing GPU resources across models, but this sharing also makes energy optimization difficult. Multiple co-resident models run under one device-wide operating point, while their resource demands and latency slack change across execution phases and load conditions. As a result, minimizing energy requires coordinated scheduling across request placement, runtime resource adaptation, and workload consolidation. We present Festina, a profiling-guided, power-aware control plane to minimize cluster-wide energy for serverless LLM serving. Unlike common global-local schedulers that focus on throughput or tail latency, Festina makes energy-first decisions by jointly coordinating request placement, SM partitioning, and GPU operating points under TTFT/TBT SLOs. In our system, a lightweight global scheduler performs fast, SLO-safe, energy-aware placement using constant-time lookups from offline profiles and GPU state summaries. On each GPU, a phase-aware local scheduler continuously adapts task batching and compute resources to minimize power consumption. Festina further performs energy-aware workload consolidation to reduce GPUs' static power consumption via SLO-aware migration. Comparison with four SOTA LLM serving systems and one DVFS-augmented system demonstrates that Festina reduces energy consumption by up to 56% while maintaining parity in SLO attainment (within a 2% margin)
No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.
No ranking explanation is available yet.
No tags.
@article{wang2026energy,
title = {Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs},
author = {Tianyu Wang and Gourav Rattihalli and Aditya Dhakal and Longfei Shangguan and Dejan Milojicic},
year = {2026},
abstract = {As LLM inference becomes a major cloud workload, its growing energy footprint makes cluster-wide energy optimization increasingly important. Serverless LLM serving helps platforms absorb traffic volatility by elastically sharing GPU resources across models, but this sharing also makes energy optimization difficult. Multiple co-resident models run under one device-wide operating point, while their resource demands and latency slack change across execution phases and load conditions. As a result, },
url = {https://arxiv.org/abs/2606.30391},
keywords = {cs.DC},
eprint = {2606.30391},
archiveprefix = {arXiv},
}
{}