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

Guilin Zhang, Wulan Guo, Ziqi Tan

TL;DR

This work tackles the problem of efficiently deploying multi-agent collaborative reasoning on serverless GPU platforms, where heterogeneous agent workloads and latency requirements collide with cost and capacity constraints. It proposes an adaptive GPU resource allocation framework with workload-aware, priority-based scheduling and an $O(N)$ real-time algorithm to distribute GPU resources among agents. Through simulations with four heterogeneous agents, the approach delivers up to 85% latency reduction compared to round-robin while maintaining aggregate throughput similar to static allocations and with identical costs, validating robustness across varying workloads. The study provides practical deployment guidelines and identifies future directions, including predictive workload modeling, inter-GPU scheduling, and hierarchical orchestration across cluster levels for real-world production environments.

Abstract

Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficiently deploying these systems on serverless GPU platforms presents significant resource allocation challenges due to heterogeneous agent workloads, varying computational demands, and the need for cost-effective scaling. This paper presents an adaptive GPU resource allocation framework that achieves 85\% latency reduction compared to round-robin scheduling while maintaining comparable throughput to static allocation, using an $O(N)$ complexity algorithm for real-time adaptation. Our approach dynamically allocates GPU resources based on workload characteristics, agent priorities, and minimum resource requirements, enabling efficient utilization while maintaining quality of service. The framework addresses three key challenges: (1) heterogeneous computational demands across lightweight coordinators and heavyweight specialists, (2) dynamic workload fluctuations requiring millisecond-scale reallocation, and (3) capacity constraints in serverless environments. Through comprehensive simulations modeling realistic multi-agent workflows with four heterogeneous agents, we demonstrate that adaptive allocation outperforms static equal and round-robin strategies across latency, cost, and GPU utilization metrics. The framework provides a practical solution for deploying cost-efficient multi-agent AI systems on serverless GPU infrastructure.

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

TL;DR

This work tackles the problem of efficiently deploying multi-agent collaborative reasoning on serverless GPU platforms, where heterogeneous agent workloads and latency requirements collide with cost and capacity constraints. It proposes an adaptive GPU resource allocation framework with workload-aware, priority-based scheduling and an real-time algorithm to distribute GPU resources among agents. Through simulations with four heterogeneous agents, the approach delivers up to 85% latency reduction compared to round-robin while maintaining aggregate throughput similar to static allocations and with identical costs, validating robustness across varying workloads. The study provides practical deployment guidelines and identifies future directions, including predictive workload modeling, inter-GPU scheduling, and hierarchical orchestration across cluster levels for real-world production environments.

Abstract

Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficiently deploying these systems on serverless GPU platforms presents significant resource allocation challenges due to heterogeneous agent workloads, varying computational demands, and the need for cost-effective scaling. This paper presents an adaptive GPU resource allocation framework that achieves 85\% latency reduction compared to round-robin scheduling while maintaining comparable throughput to static allocation, using an complexity algorithm for real-time adaptation. Our approach dynamically allocates GPU resources based on workload characteristics, agent priorities, and minimum resource requirements, enabling efficient utilization while maintaining quality of service. The framework addresses three key challenges: (1) heterogeneous computational demands across lightweight coordinators and heavyweight specialists, (2) dynamic workload fluctuations requiring millisecond-scale reallocation, and (3) capacity constraints in serverless environments. Through comprehensive simulations modeling realistic multi-agent workflows with four heterogeneous agents, we demonstrate that adaptive allocation outperforms static equal and round-robin strategies across latency, cost, and GPU utilization metrics. The framework provides a practical solution for deploying cost-efficient multi-agent AI systems on serverless GPU infrastructure.
Paper Structure (20 sections, 2 equations, 2 figures, 2 tables, 1 algorithm)

This paper contains 20 sections, 2 equations, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multi-Agent LLM Systems
  4. Serverless GPU Computing
  5. GPU Resource Allocation and Scheduling
  6. Cloud Resource Allocation and Autoscaling
  7. Distributed Inference Frameworks
  8. System Design
  9. Problem Formulation
  10. System Architecture
  11. Adaptive Allocation Algorithm
  12. Implementation Considerations
  13. Experimental Evaluation
  14. Experimental Setup
  15. Simulation Methodology
  16. ...and 5 more sections

Figures (2)

  • Figure 1: System architecture showing the adaptive GPU resource allocation framework with four heterogeneous agents (coordinator and three specialists) and dynamic resource distribution based on workload demand and priorities.
  • Figure 2: Performance comparison of resource allocation strategies. (a) Average latency per agent showing adaptive allocation maintains balanced latency across agents. (b) Throughput comparison demonstrating effective resource utilization. (c) Dynamic GPU allocation over time illustrating adaptive strategy's responsiveness to workload demands. (d) Cost-performance trade-off analysis with cost annotations.