Adaptive Learning-Based Distributed System Resource Scheduling

Abstract

This paper presents a novel approach to distributed system resource scheduling based on adaptive learning. Traditional resource scheduling methods often rely on static rules and predefined policies, which struggle to adapt to the dynamic and unpredictable nature of distributed environments. Our proposed system leverages reinforcement learning (RL) to train a dynamic scheduler capable of optimizing resource allocation based on real-time system load and resource state information. The core claim of this work is that utilizing adaptive learning algorithms enables the dynamic scheduling of distributed system resources. The core mechanism involves training a scheduler using RL, which learns to assign resources based on system load and resource status. This approach overcomes the limitations of static scheduling methods by providing a responsive and intelligent system that can dynamically adjust to changing demands. This paper outlines the system architecture, the RL training process, and the evaluation framework. ---