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Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments

🕒 Published (v1): 2025-12-15 09:21 UTC · Source: Arxiv · link

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TL;DR

This paper proposes an adaptive GPU resource allocation framework for multi-agent LLM systems on serverless platforms, using an O(N) priority-weighted demand algorithm. It achieves 85% latency reduction versus round-robin scheduling while matching static allocation throughput at identical cost, validated through simulation.

Problem

Multi-agent LLM systems have heterogeneous GPU demands (lightweight coordinators vs. heavyweight specialists), yet serverless GPU platforms lack allocation strategies tuned for this workload mix. Static equal allocation and round-robin scheduling both fail: the former ignores demand asymmetry, the latter serializes concurrently-needed agents and causes queue buildup.

Method

The framework models each agent by model size, base throughput, minimum GPU fraction, and priority level. At each timestep, a demand score is computed per agent as d_i = λ_i(t) · R_i / P_i (arrival rate × min GPU requirement ÷ priority), resources are allocated proportionally to these scores, minimum requirements are enforced to prevent starvation, and the total is renormalized if it exceeds capacity. The algorithm runs in O(N) time, enabling sub-millisecond reallocation. No learning is involved—the allocation is a closed-form calculation. The evaluation is simulation-only, modeling four agents (coordinator + NLP/Vision/Reasoning specialists) on a synthetic 100-second workload targeting NVIDIA T4 characteristics.

Key Contributions

  • Priority-weighted demand metric combining arrival rate, minimum resource floor, and agent priority into a single O(N) allocation signal.
  • Enforcement of per-agent minimum GPU floors within a normalized capacity constraint, preventing starvation under overload.
  • Simulation study comparing adaptive, static-equal, and round-robin strategies across latency, throughput, cost, and queue-length metrics.
  • Deployment guidelines: priority configuration for coordinators vs. specialists, minimum GPU sizing by model size.

Results

  • Adaptive avg latency: 111.9s vs. Static Equal 110.3s vs. Round-Robin 756.1s — 85% latency reduction vs. round-robin.
  • Total throughput: Adaptive 58.1 rps vs. 60.0 rps for both baselines (3.2% reduction).
  • Cost identical across all three strategies: $0.020 per 100 seconds.
  • Latency std dev: Adaptive 3.8s vs. Static 4.2s vs. Round-Robin 0.5s.
  • Under 3× demand overload: latency degrades by 24% with no starvation; 10× spike handled within 100ms; allocation computation < 1ms.
  • Reasoning specialist (highest priority) achieves lowest per-agent latency (91.6s); vision specialist highest (128.6s).

Limitations

  • Evaluation is entirely simulation-based; no empirical validation on real serverless GPU hardware.
  • The four-agent topology and workload parameters are synthetic and fixed; generalizability to larger or differently structured agent graphs is undemonstrated.
  • The demand formula is heuristic with no learned or adaptive weighting; α, β, γ cost-function weights are not tuned empirically.
  • No inter-agent communication or dependency modeling—agents are treated as independent queues, which understates coordination overhead in real workflows.
  • Round-robin is a weak baseline; no comparison to existing GPU schedulers from related work (e.g., RL-based or pipeline-conscious methods).

Relevance to Harnesses / Meta-Harnesses

This work directly addresses the infrastructure layer that multi-agent harnesses execute on: how to allocate shared GPU capacity across heterogeneous agent roles (orchestrators vs. specialists) in real time. For harness designers, the priority-and-minimum-floor model maps naturally onto the coordinator/worker role split common in meta-harness architectures. The O(N) recalculation loop is analogous to a scheduling tick in a task dispatcher, making this a concrete reference for how a harness runtime could expose resource hints to an underlying serverless scheduler. The gap between simulation and production deployment, however, means practitioners should treat the numbers as directional rather than prescriptive.