HyperFlexis: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling

• URL: http://arxiv.org/abs/2508.15919v2
• Date: Thu, 25 Sep 2025 03:00:22 GMT
• Title: HyperFlexis: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling
• Authors: Zahra Yousefijamarani, Xinglu Wang, Qian Wang, Morgan Lindsay Heisler, Taha Shabani, Niloofar Gholipour, Parham Yassini, Hong Chang, Kan Chen, Qiantao Zhang, Xiaolong Bai, Jiannan Wang, Ying Xiong, Yong Zhang, Zhenan Fan,
• Abstract summary: Modern large language model (LLM) serving systems face challenges from highly variable requests with diverse lengths, priorities, and stage-specific service-level objectives (SLOs)<n>We present HyperFlexis, a unified LLM serving system that integrates algorithmic and system-level innovations to jointly optimize scheduling and scaling under multiple SLOs.
• Score: 19.154782641360253
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern large language model (LLM) serving systems face challenges from highly variable requests with diverse lengths, priorities, and stage-specific service-level objectives (SLOs). Meeting these requires real-time scheduling, rapid and cost-effective scaling, and support for both collocated and disaggregated Prefill/Decode (P/D) architectures. We present HyperFlexis, a unified LLM serving system that integrates algorithmic and system-level innovations to jointly optimize scheduling and scaling under multiple SLOs. It features a multi-SLO-aware scheduler that leverages budget estimation and request prioritization to ensure proactive SLO compliance for both new and ongoing requests. The system supports prefill- and decode-stage multi-SLO scheduling for P/D-disaggregated architectures and KV cache transfers. It also enables cost-effective scaling decisions, prefill-decode instance linking during scaling, and rapid P/D role transitions. To accelerate scaling and reduce cold-start latency, a device-to-device (D2D) weight transfer mechanism is proposed that lowers weight loading overhead by up to 19.39$\times$. These optimizations allow the system to achieve up to 4.44$\times$ higher SLO attainment, 65.82% lower request latency, and cost parity with state-of-the-art baselines. The code will be released soon.

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