# SkyWalker: A Locality-Aware Cross-Region Load Balancer for LLM Inference

arXiv: https://arxiv.org/abs/2505.24095 · HTML: https://mlsanity.com/paper/2505.24095/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2025.

## Built on by (most cited first, 10 of 10)

- [Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live](https://mlsanity.com/paper/2511.02230/) (2025)
- [TokenScale: Timely and Accurate Autoscaling for Disaggregated LLM Serving with Token Velocity](https://mlsanity.com/paper/2512.03416/) (2025)
- [Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference](https://mlsanity.com/paper/2508.19559/) (2025)
- [Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective](https://mlsanity.com/paper/2601.18999/) (2026)
- [Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation](https://mlsanity.com/paper/2606.01725/) (2026)
- [SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling](https://mlsanity.com/paper/2607.08565/) (2026)
- [GORGO: Online Tuning for Cross-Region Network-Aware LLM Serving](https://mlsanity.com/paper/2602.11688/) (2026)
- [A Predictive and Synergistic Two-Layer Scheduling Framework for LLM Serving](https://mlsanity.com/paper/2509.23384/) (2025)
- [Accuracy Is Speed: Towards Long-Context-Aware Routing for Distributed LLM Serving](https://mlsanity.com/paper/2604.15732/) (2026)
- [Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving](https://mlsanity.com/paper/2610.14571/) (2026)

## Abstract (arXiv)

Serving Large Language Models (LLMs) efficiently in multi-region setups remains a challenge. Due to cost and GPU availability concerns, providers typically deploy LLMs in multiple regions using instance with long-term commitments, like reserved instances or on-premise clusters, which are often underutilized due to their region-local traffic handling and diurnal traffic variance. In this paper, we introduce SkyWalker, a multi-region load balancer for LLM inference that aggregates regional diurnal patterns through cross-region traffic handling. By doing so, SkyWalker enables providers to reserve instances based on expected global demand, rather than peak demand in each individual region. Meanwhile, SkyWalker preserves KV-Cache locality and load balancing, ensuring cost efficiency without sacrificing performance. SkyWalker achieves this with a cache-aware cross-region traffic handler and a selective pushing based load balancing mechanism. Our evaluation on real-world workloads shows that it achieves 1.12-2.06x higher throughput and 1.74-6.30x lower latency compared to existing load balancers, while reducing total serving cost by 25%.