Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs

ElastiCo: Elastic Configuration and Interference-Aware Orchestration for GPU Clusters

Modern GPU clusters must simultaneously serve deep learning training and offline large language model inference workloads, yet existing schedulers treat these as isolated resource consumers with rigid, static allocations. This leaves substantial GPU capacity underutilized: training jobs reserve entire devices despite periodic idle phases, while offline inference tasks over-provision GPUs despite bursty demand patterns. We present ElastiCo, an elastic co-location framework that enables training and inference workloads to safely share GPUs through three integrated mechanisms. First, Resource Shape Transformation exposes each job as a family of feasible resource-performance configurations. Second, Elastic Shadow Pricing decomposes the resulting multi-resource allocation problem into per-job configuration selection subproblems via dynamic per-resource shadow prices. Third, Interference-Aware Co-location uses a predictor trained on hardware-counter and task-level features to estimate pairwise performance degradation under GPU sharing. Implemented as native Kubernetes middleware requiring no user-code modifications, ElastiCo is evaluated on a 64-GPU testbed and through large-scale trace-driven simulations (up to 512 GPUs), reducing the average JCT by up to 2.94x, increasing the cluster throughput by 2.02x, and increasing the GPU utilization from approximately 25% to 46%.

DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes

Energy efficiency has become a first-order concern in modern high-performance computing systems, as it directly determines achievable throughput under fixed power budgets. Although Dynamic Voltage and Frequency Scaling (DVFS) provides an effective mechanism for reducing GPU energy consumption, existing runtime systems decouple DVFS from task placement and inter-GPU communication, focus on single-GPU execution, or cannot adapt frequency to task granularity and runtime contention in multi-GPU environments. Consequently, current schedulers fail to capture the tight coupling between task placement, frequency selection, and inter-GPU data movement that fundamentally governs energy–performance trade-offs on multi-GPU systems. This paper presents DEFT, an energy-aware scheduling framework that jointly optimizes task-to-device assignment and per-GPU DVFS configuration for task-based multi-GPU applications. DEFT employs a cost-model–driven strategy that integrates slack awareness, throughput awareness, and explicit modeling of task execution cost, inter-GPU data movement, and DVFS transition overheads, enabling coordinated placement and frequency decisions at task granularity under dynamic runtime conditions. We prototype DEFT within the CUDASTF runtime and demonstrate its effectiveness across five optimization objectives. The evaluation shows that DEFT reduces energy consumption by 14.8% and 4.8% on average on NVIDIA L40S and L4, and reduces EDP by 9.9% and 3.7%, respectively, while maintaining performance within 1.5% of the fastest baseline.

CELLServe: An SLO-Aware and Cost Efficient LLMs Serving System for Serverless Computing Environments

CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.

Sparsity-Aware Scheduling for Efficient MoE-Based LLM Serving on Edge Servers

A Tepid-Aware container scheduler for serverless computing environments targeting throughput and energy efficiency

Theseus: Runtime-Adaptive GPU Collective Communication with Hot-Swappable Schedules

Current GPU Collective Communication Libraries (CCLs) employ predefined schedules optimized for stable environments. Their supported schedules and selection logic are fixed at communicator initialization, which fails to account for evolving runtime conditions, such as workload characteristics and hardware health status. Consequently, long-running GPU jobs experience suboptimal performance after hours or days of execution, which translates into longer job completion times and wasted GPU cluster resources. To address this problem, we present Theseus, a novel CCL backend that provides schedule-level runtime adaptivity. It admits user-defined schedules and selection policies. As runtime conditions change, Theseus selects suitable schedules using cluster-wide runtime attributes beyond CCL-internal metrics. Moreover, it hot-swaps from the previous schedule consistently across GPUs with low overhead. Theseus acts as a drop-in replacement to facilitate integration. We evaluate Theseus extensively on various GPU workloads with intuitive policies. Compared with NCCL, Theseus achieves up to 1.61X speedup of communication time in stable environments and 2.46X in dynamic environments. It improves end-to-end job completion time by up to 1.84X while incurring comparable or lower overhead.