Distributed Edge Computing Resource Scheduling and Optimization
Abstract
This paper investigates the challenges and proposes a novel approach to distributed edge computing resource scheduling and optimization. The core claim is to design an efficient algorithm that dynamically allocates resources based on task requirements and edge computing node capabilities, ultimately enhancing computational efficiency. The proposed method utilizes a reinforcement learning (RL)-based resource scheduling algorithm, learning an optimal resource allocation policy through iterative interaction with the environment. The paper details the algorithm's architecture, the RL framework employed, and presents a theoretical analysis of its performance. The objective is to address the complexities inherent in managing heterogeneous edge environments, optimizing resource utilization, and minimizing latency for applications. Simulation results demonstrate the algorithm's effectiveness in achieving superior performance compared to traditional scheduling approaches. ---
