Ruiqi Lai, Hongrui Liu, Chengzhi Lu, Zonghao Liu, Siyu Cao, Siyang Shao, Yixin Zhang, Luo Mai 等 9 位作者
The architectural shift to prefill/decode (PD) disaggregation in LLM serving improves resource utilization but struggles with the bursty nature of modern workloads. Existing autoscaling policies, often retrofitted from monolithic systems like those in AIBrix and DistServe, rely on lagging indicators such as GPU utilization or coarse-grained request counts. This results in slow reactions to load spikes, leading to significant Time-to First-Token (TTFT) and Time-Per-Output-Token (TPOT) SLO violations and costly over-provisioning. We introduce TokenScale, an autoscaling framework that resolves this performance mismatch through two innovations. First, we propose Token Velocity, a novel metric that unifies the prefill, network, and decode stages by quantifying their rate of work. As a leading indicator of system backpressure, it enables proactive scaling. Second, Convertible Decoders allow decoder GPUs to dynamically execute prefill tasks during traffic spikes, creating a rapid-response buffer that absorbs bursts and eliminates the initialization latency of new prefillers. Our evaluation on a GPU cluster with production traces shows TokenScale improves SLO attainment from 50-88% to 80-96% and reduces costs by 4-14% over state-of-the-art systems, including DistServe, BlitzScale, and AIBrix. By uniting a predictive metric with a flexible system design, TokenScale significantly boosts the performance and efficiency of disaggregated LLM serving infrastructure.
Jooyoung Park, Leonid Kondrashov, Boxi Zhou, Hongrui Liu, Zonghao Liu, Chengzhi Lu, Riccardo Mancini, Esha Choukse 等 17 位作者
Autonomous agents challenge conventional LLM serving with long-lived, cross-component execution and evolving system state. These characteristics complicate controlled systems experimentation and attribution of end-to-end behavior across the serving stack. We present Aries, a full-stack experimentation framework that separates task specifications from execution configurations, reconstructs cross-component agent trajectories with correlated system telemetry, and exposes stateful tool execution through a consistent interface across heterogeneous sandbox substrates. We use Aries to conduct reproducible experiments on open agent harnesses and benchmarks. We complement these experiments with production traces from a commercial platform, grounding low-level systems research in observed production behavior. Our results show that (1) token-centric metrics miss non-inference bottlenecks, (2) retaining additional context yields diminishing accuracy benefits while reducing serving capacity, and (3) tool sandboxes alternate between long idle periods and short resource bursts, while current snapshot-based state management makes aggressive suspension costly. We then discuss the vision for agent-native serving systems designed around trajectory-level metrics, adaptive context management, and elastic sandbox resource management.
LLM agents repeatedly send a growing conversation to a model, making prefix reuse central to efficient serving. We show that a brief surge can disrupt this reuse and trigger metastable failure in prefill--decode (PD) disaggregated engines: service remains degraded after arrivals return to a load the engine previously sustained. On SGLang, a ten-minute surge reduced goodput by about 88% over the following hour. Removing the residual burst sessions restored service within minutes. The sustaining mechanism is population-cache feedback. Slower calls prolong sessions and enlarge their aggregate context, or working set; the resulting cache misses increase recomputation and further delay departures. A fluid model identifies the resource conditions for this feedback and explains how cache capacity, tool time and base load affect vulnerability. Across 36 simulated deployments, it flags all 17 cache-first cases. Controlled simulations hold the post-surge state fixed and show that changing subsequent arrivals alone reverses the recovery outcome. Once the cache collapses, low-reuse departure capacity sets the scale of the load reduction needed for recovery. Guided by these dynamics, we build Basin, which gives each session a fixed priority, reserves their predicted peak contexts against an adaptive admission budget, extends the cache with idle decode memory, and sheds excess demand. On real H200 1P3D deployments under surges, including long-context SWE-bench Pro sessions, an arrival-time prototype achieves 1.20--1.70x the goodput of Continuum and 2.35--3.59x that of HiCache alone. In simulation, the predictor-driven policy delivers 1.20--2.05x the strongest baseline's goodput across six surge scenarios.