Adaptive Resource Allocation via Multi-Agent Reinforcement Learning for Dynamic Load Balancing in AI Workloads

Abstract

This paper explores the application of multi-agent reinforcement learning (MARL) to address the challenge of dynamic load balancing in AI workloads. Modern AI applications often involve complex computational tasks that require efficient resource allocation across distributed systems. Traditional load balancing techniques struggle to adapt to the rapidly changing demands and heterogeneous nature of these workloads. We propose a novel MARL framework where intelligent agents collaboratively learn to optimize resource allocation decisions in real-time. Each agent is responsible for managing a subset of resources and interacts with its environment to learn effective strategies for distributing tasks. We design a reward function that encourages efficient resource utilization, minimizes task completion times, and promotes fairness among agents. Our experiments demonstrate that the proposed MARL approach significantly outperforms conventional load balancing algorithms in terms of overall system throughput, task latency, and resource utilization. We also analyze the emergent behavior of the agents and provide insights into the learned allocation strategies. The results highlight the potential of MARL for achieving dynamic and adaptive load balancing in complex AI-driven environments.