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

Abstract

Large Language Models (LLMs) have enabled diverse AI applications. However, LLMs inference impose unprecedented computational and memory overhead, creating an inherent trade-off between latency Service Level Objectives (SLOs) and resource constraints. Serverless computing, with on-demand provisioning and pay-as-you-go billing, is becoming a promising paradigm for LLM serving. But existing solutions fail to integrate state-of-the-art inference optimizations, resulting in suboptimal GPU utilization and prolonged latency. While Prefill-Decode (PD) disaggregation combined with continuous batching has resolved such inefficiencies in traditional cloud deployments, migrating these techniques to serverless makes two challenges particularly pronounced: (1) SLO-constrained resource provisioning for independently scaling prefill and decode phase functions, and (2) function lifespan management to mitigate resource waste from continuous batching-induced prolonged instance lifespans. To tackle these issues, we propose CELLServe, an SLO-aware and cost-efficient serverless LLM serving system that pioneers integrating PD disaggregation and continuous batching into serverless platforms. 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. Comprehensive evaluations on five mainstream LLMs and real-world traces show that CELLServe achieves 1.85×–1.92×higher request throughput than baselines under identical SLOs and GPU budgets, while sustaining high resource efficiency under dynamic workloads.