This work extends Llumnix's priority model to support an arbitrary number of tiers and evaluates the effects of this extension under three realistic priority distributions (uniform, Gaussian, enterprise) using Vidur, a high-fidelity LLM inference simulator.
Abstract
Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing. Llumnix introduced a dynamic, migration-capable multi-instance scheduler for LLM inference that achieves load balancing, defragmentation, prioritization, and auto-scaling through a unified"freeness"metric. However, Llumnix's priority model is restricted to two levels (high and normal), an abstraction too coarse to express the richer SLA classes common in production deployments. In this work, we extend Llumnix's priority model to support an arbitrary number of tiers and evaluate the effects of this extension under three realistic priority distributions (uniform, Gaussian, enterprise) using Vidur, a high-fidelity LLM inference simulator. We implement per-tier headroom with exponential decay, tier-aware dispatch ordering, and the full Llumnix migration pipeline inside Vidur's hierarchical scheduling framework. We compare our extended scheduler against INFaaS (global routing baseline), vLLM, Orca, and Sarathi-Serve (per-replica baselines), sweeping priority levels from 1 to 10. Our experiments demonstrate that four priority tiers yields the best cost-effectiveness tradeoff, achieving prefill mean speedups of up to 8.3x and end-to-end P99 speedups of up to 3.1x over INFaaS with cost-per-latency improvements of 46 to 68%, while preserving strong SLO differentiation across tiers. We further show that the system sustains these gains at 10 priority levels without tail latency collapse, with overhead concentrated in the prefill phase.
Cascade, an LLM serving system that estimates and continuously updates this per-request latency budget from request characteristics, KV-cache state, and current system load, and uses a single per-request budget to jointly coordinate request scheduling and KV-cache management across the memory hierarchy.
Muhammad Adnan, R. Mahapatra, Prashant J. Nair et al.· 0 citations
The widespread adoption and strong generalizability of large language models (LLMs) lead to highly heterogeneous workloads that exhibit substantial variability in request lengths and latency requirements. This pronounced heterogeneity causes existing scheduling strategies to suffer from head-of-line blocking and inefficient request ordering, resulting in significant SLO violations and inflated end-to-end latency. In this paper, we present UNAS, a hierarchical scheduler for end-to-end SLO-oriented LLM serving. UNAS adopts a two-level design. At the cluster level, it classifies incoming requests into urgent and normal queues based on estimated service times derived from request features and runtime statistics, and dynamically updates their queue priorities as latency pressure evolves. At the instance level, UNAS performs fine-grained dispatch using a least-slack-first policy, prioritizing requests with the highest risk of SLO violations. Experimental results demonstrate that UNAS consistently outperforms representative baselines across diverse workloads, improving end-to-end SLO attainment by up to 11.1% and reducing mean end-to-end latency by up to $1.47 \times$.
Tian-Nan Fu, Jianxiong Liao, Xu Chen et al.· Fall Joint Computer Conferen...· 0 citations
A framework is intended as a design reference for practitioners building inference gateways and as a structuring basis for future empirical work on multi-objective LLM request scheduling, and an evaluation protocol comprising workload definitions, baselines, and metrics is specified.
Rajalakshmi Srinivasaraghavan· International journal of com...· 0 citations
Deploying large language models (LLMs) on edge nodes enables low-latency and privacy-preserving inference, but faces severe resource constraints under high-concurrence workloads. While existing inference systems leverage intranode key–value (KV) caching to improve efficiency, they largely neglect the unique complexities of multinode edge environments. Specifically, reactive KV cache eviction policies suffer from temporal uncertainty, often discarding reusable KV caches prematurely, while the tight coupling between request scheduling and cache placement often leads to myopic decisions that exacerbate load imbalance and resource contention. To address these challenges, we propose a dynamic block-level paradigm that treats KV blocks as the fundamental units for caching and scheduling, enabling dynamic sharing, generation, and eviction of arbitrary-length prefixes. We present complete modeling of the spatiotemporal coupling between scheduling and caching under block-level granularity, capturing intricate interactions overlooked by prior work. Based on this model, we design an online joint optimization algorithm, which applies to general edge LLM serving scenarios. The algorithm decouples spatiotemporal dependencies via randomized rounding over per-slot subproblems, achieving a balance between real-time responsiveness and long-term system efficiency. Theoretical analysis establishes high-probability near-optimality guarantees, and extensive experiments show that our method reduces the average time to first token (TTFT) by up to 54.02% over existing baselines.
Xishuo Li, Wei Jiao, Jun He et al.· IEEE Internet of Things Jour...· 0 citations
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.
Zejian Wang, Nan Lin, Zinuo Cai et al.· ACM Transactions on Architec...· 0 citations
OpScale is presented, a practical operator-level orchestration framework of profiling, provisioning, placement, and runtime serving that attains SLOs with up to 36.3% fewer GPUs and 28% less power, or achieves 44% higher throughput under fixed cost budgets.
Xingqi Cui, Chieh-Jan Mike Liang, Ziang Tang et al.· 0 citations