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This study investigates the effectiveness of large language models (LLMs) as schedulers for deadline-aware, mixed-criticality task scheduling in mobile edge computing (MEC) for autonomous vehicles. By comparing a strong heuristic based on a windowed contract-net auction against an LLM control layer, the authors find that while the heuristic performs optimally under stationary conditions, the LLM control plane significantly outperforms it during non-stationary task surges. The results indicate that LLMs provide value primarily in dynamic environments where task demands fluctuate, highlighting their potential role in enhancing scheduling efficiency in critical applications.
LLMs only prove their worth in task scheduling when faced with unpredictable surges of safety-critical demands, revealing their limitations in stable environments.
Autonomous vehicles offload latency-sensitive perception tasks to nearby mobile edge computing (MEC) servers, where a missed safety-critical task is unsafe rather than merely degraded. Large language models (LLMs) are increasingly proposed as adaptive, explainable schedulers, yet evidence of when they help is scarce. We study deadline-aware, mixed-criticality scheduling on heterogeneous MEC servers, where time-critical (TC) tasks must be protected at a controlled cost to best-effort traffic, and ask whether a multi-agent LLM control layer improves on a strong heuristic. We answer in two steps. First we build the heuristic: a windowed contract-net auction that orders each admission window time-critical-first by earliest deadline and places tasks by earliest-finish-time. Across 60 instances on three topologies and 15 baselines under an identical online constraint, it attains a TC completion rate of 0.902, above every baseline (Holm-corrected p<0.001; best baseline 0.838) and at 0.87 of a CP-SAT upper bound. Second, we add the LLM control plane. A controlled decomposition traces the scheduler's advantage to two ordinary factors, the batching horizon and time-critical-first ordering; the auction, the per-window LLM policy, and online adaptation add nothing while the load is stationary, where the heuristic is already near-optimal. Under a mid-run surge of safety-critical tasks the picture changes, and the LLM control plane gains significantly over both the static heuristic and the bandit. LLM orchestration therefore earns its cost only when non-stationarity opens headroom a fixed policy cannot use. We report control-plane latency and rationale, and release all code and seeded instances.
