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Workload-Adapted Resource Allocation for LLM Distributed Serving in Serverless Clusters
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DOI：10.1109/TPDS.2026.3683122.png)
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Abstract
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Large language models increasingly rely on pipeline parallelism for distributed inference, but existing systems face critical challenges in serverless environments: heterogeneous request distributions across pipeline stages and unpredictable workload patterns requiring rapid elasticity. Traditional static resource allocation fails to address pipeline-specific bottlenecks and cold start delays inherent in serverless architectures. We propose Quart, a workload-adapted resource allocation system for LLM distributed serving in serverless clusters. Quart introduces pipeline-aware resource management through: (1) latency-aware critical stage identification using coefficient of variation (CV)-based burst propagation analysis, (2) dynamic replica allocation with proportional-integral-derivative (PID) control for congested stages, and (3) hierarchical parameter caching with copy-on-write mechanisms enabling sub-second serverless scaling. The system addresses serverless-specific challenges through cache-aware scheduling that maintains model parameters in memory, eliminating disk I/O overhead during rapid scaling events. Evaluation with real-world workloads shows Quart reduces average response latency by up to 87.1% compared to existing serverless inference systems while achieving 2.37x improvement in goodput.
Keywords:
Pipeline inference
large language model
serverless
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