Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees

• URL: http://arxiv.org/abs/2506.19677v2
• Date: Wed, 25 Jun 2025 16:13:14 GMT
• Title: Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees
• Authors: Shi Chang, Boyuan Chen, Kishanthan Thangarajah, Hanan Lutfiyya, Ahmed E. Hassan,
• Abstract summary: Existing Large Language Models (CodeMs) are increasingly integrated into modern software development.<n>Yet, self-hosted environments remain a significant challenge in resource-constrained serving environments.<n>We propose SABER, a dynamic strategy that predicts per-request SLA feasibility and decisions in real time.<n>Our results demonstrate that SLA-aware, adaptive scheduling is key to robust, high-performance CodeLL serving.
• Score: 6.110847503516972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code Large Language Models (CodeLLMs) are increasingly integrated into modern software development workflows, yet efficiently serving them in resource-constrained, self-hosted environments remains a significant challenge. Existing LLM serving systems employs Continuous Batching for throughput improvement. However, they rely on static batch size configurations that cannot adapt to fluctuating request rates or heterogeneous workloads, leading to frequent SLA (Service Level Agreement) violations and unstable performance. In this study, We propose SABER, a dynamic batching strategy that predicts per-request SLA feasibility and adjusts decisions in real time. SABER improves goodput by up to 26% over the best static configurations and reduces latency variability by up to 45%, all without manual tuning or service restarts. Our results demonstrate that SLA-aware, adaptive scheduling is key to robust, high-performance CodeLLM serving.

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