Geometry-Aware Online Scheduling for LLM Serving: From Theoretical Bound to System Practice

Researchers propose Geometry-Aware Online Scheduling, introducing the Smallest Volume First (SVF) algorithm to optimize LLM inference by accounting for dynamic memory footprint of Key-Value caches. The approach improves upon traditional time-centric scheduling heuristics, achieving significant reductions in latency and throughput gains when integrated into vLLM.

This research addresses a fundamental bottleneck in LLM inference systems: the management of Key-Value cache memory during serving. While existing inference engines rely on time-based scheduling heuristics like Shortest Job First, these approaches fail to capture the unique 2D spatio-temporal characteristics of LLM memory consumption. The proposed SVF algorithm represents a meaningful theoretical advance, tightening the worst-case competitive ratio from 48 to 5, which signals substantial improvements in predictable performance under stress conditions.

The work emerges from the rapidly expanding LLM serving infrastructure landscape, where inference efficiency directly impacts operational costs and user experience. As organizations deploy increasingly large models like Llama-3.1, memory constraints become a critical limiting factor. Current scheduling methods were designed for traditional computing paradigms that don't account for the geometric growth pattern of LLM attention mechanisms, making them fundamentally misaligned with modern workload characteristics.

For industry stakeholders, this research offers practical implications. The 1-bit SVF variant demonstrates that sophisticated memory optimization requires minimal information overhead, lowering implementation barriers for existing systems. Integration as a plug-and-play layer in vLLM—a widely-adopted open-source inference engine—means this optimization could rapidly proliferate across deployments. Demonstrated latency improvements directly reduce infrastructure costs and enable better user experience for interactive applications.

The open-source release of the implementation accelerates adoption and encourages further optimization work in this space. As LLM inference becomes increasingly cost-competitive, scheduling efficiency transitions from a performance nice-to-have to a critical differentiator for inference providers.

• →SVF algorithm tightens worst-case performance bounds from CR≤48 to CR≤5, representing significant theoretical and practical improvements
• →Novel scheduling approach accounts for 2D spatio-temporal geometry of LLM Key-Value cache memory, unlike traditional time-centric heuristics
• →1-bit SVF achieves competitive results with minimal information requirements, enabling seamless integration into existing inference systems
• →Integration with vLLM demonstrates broad applicability across popular open-source LLM serving infrastructure
• →Results show consistent reductions in both average and tail latency on Llama-3.1 models, indicating robust performance across workload distributions