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

💡 Analysis

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.

📄 Content

The rapid advancement of large language models (LLMs) has catalyzed the emergence of multi-agent systems as a powerful paradigm for tackling complex reasoning tasks that exceed the capabilities of individual models [1], [2]. These systems leverage multiple specialized AI agents working collaboratively, with each agent focusing on specific aspects of problem-solving such as natural language understanding, visual reasoning, or logical inference. Recent research demonstrates that multi-agent collaboration can significantly enhance performance across diverse applications including code generation, scientific reasoning, and decision support systems [3], [4]. The complexity of coordinating multiple agents with heterogeneous resource requirements has sparked Corresponding author: guilin.zhang@gwu.edu growing interest in adaptive resource allocation strategies [5], [6].

Concurrently, serverless computing has revolutionized cloud infrastructure by enabling automatic scaling, pay-peruse pricing, and simplified deployment [7], [8]. The integration of GPU acceleration into serverless platforms has further expanded possibilities for deploying computationally intensive AI workloads. Major cloud providers including Google Cloud Run, Azure Container Apps, and specialized platforms now offer serverless GPU support with sub-second cold start times and fine-grained billing [9], [10]. Recent advances in serverless GPU systems address challenges including resource-on-demand provisioning [11], pipeline-conscious scheduling [12], and fast container setup [13].

Despite these advances, deploying multi-agent systems on serverless GPU infrastructure presents unique challenges. Multi-agent workflows exhibit heterogeneous demands [6], [14]: lightweight coordinators require minimal GPU for orchestration, while specialists demand substantial compute. Traditional static and round-robin allocation strategies fail to address this heterogeneity, leading to resource underutilization and latency variations. The fundamental challenge lies in dynamically allocating limited GPU resources across multiple agents with competing requirements while minimizing costs and maintaining quality of service. Existing GPU scheduling research has primarily focused on either singlemodel inference optimization [15], [16] or multi-tenant batch training workloads [17], [18]. Recent work explores GPU multitasking for LLM workloads [19], hierarchical resource partitioning with reinforcement learning [20], and poweraware scheduling [21]. However, the unique characteristics of multi-agent collaborative reasoning, particularly the dependencies between agent interactions and the need for real-time responsiveness, remain insufficiently addressed.

This paper makes the following contributions: Adaptive Allocation Framework: We present a novel GPU resource allocation framework specifically designed for multi-agent collaborative reasoning in serverless environments. The framework dynamically adjusts resource distribution based on agent priorities, workload intensity, and minimum computational requirements.

Workload-Aware Scheduling: We propose a prioritybased scheduling algorithm that accounts for the heterogeneous nature of multi-agent systems, differentiating between latency-sensitive coordinator agents and throughput-oriented specialist agents.

Comprehensive Evaluation: Through detailed simulation studies modeling realistic multi-agent workflows, we demonstrate that our approach achieves comparable aggregate throughput to static allocation while reducing latency by 85% compared to naive round-robin strategies, all within the same cost constraints.

Practical Insights: We provide actionable guidelines for deploying multi-agent systems on serverless GPU platforms, including agent profiling methodologies and resource allocation policies that practitioners can readily apply.

The remainder of this paper is organized as follows. Section II reviews related work in multi-agent systems, serverless GPU computing, and resource allocation. Section III describes our system design and adaptive allocation algorithm. Section IV presents the experimental methodology. Section V analyzes results and discusses implications. Section VI concludes and outlines future work.

Multi-agent LLM systems have gained significant attention as surveys [1], [2] identify key patterns including cooperative problem-solving and hierarchical decomposition, enabling emergent behaviors beyond individual models. Recent work explores collaboration mechanisms [3], self-resource allocation [4], multi-agent RL optimization [5], and efficient scaling across heterogeneous systems [6]. However, these studies provide limited guidance on infrastructure and resource management for production deployments, which our work addresses through serverless GPU allocation.

Serverless GPU systems address cold starts [7], hybrid auto-scaling [8], low-latency inference [9], and efficient data transfer [10]. Recent advances includ

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