Laminar: A Probe-First Scheduling Paradigm with Deterministic Runtime Survival

TL;DR

Laminar is a decentralized scheduling framework for exascale GPU clusters that improves runtime survival and workload management through probabilistic flow splitting and a node-local survival layer.

Contribution

It introduces Laminar, a novel probe-first, execute-later scheduling paradigm with Airlock for runtime survival, enhancing lifecycle-aware scheduling at scale.

Findings

Enables near O(1) control-plane work complexity.

Provides bounded, priority-ordered runtime survival.

Improves preservation of long-resident workloads under pressure.

Abstract

In exascale-oriented GPU clusters, rigid-topology jobs leave behind a fragmented post-landing ecology in which long-resident workloads and highly transient tasks compete for unstable residual capacity. Existing centralized, hierarchical, and local-first decentralized schedulers incur growing coordination and retry-amplification costs in this regime and typically stop their explicit responsibility at execution start, leaving runtime survival to indiscriminate host-level OOM heuristics. We present Laminar, a decentralized probe-first, execute-later scheduling paradigm that keeps hot-path control-plane work near $\mathcal{O}(1)$ through Zone-level probabilistic flow splitting, bounded in-Zone probing by persistent lightweight agents, and node-local arbitration. Laminar further introduces Airlock, a bounded node-local runtime-survival layer that converts severe memory pressure into an ordered policy of suspension, in-situ recovery, bounded secondary re-addressing, or reclamation. By enforcing priority-ordered survival under pressure, Laminar enables lifecycle-aware scheduling that preserves high-value long-resident work and operates closer to physical saturation without relying on protocol-level overcommitment.