Year: 2025
Type: article
Abstract: Cloud computing environments require intelligent and efficient resource scheduling to manage dynamic workloads and meet service-level objectives. Traditional rule-based scheduling algorithms often fall short in handling the complexity and scale of modern cloud systems. This paper introduces a novel framework that leverages Explainable Artificial Intelligence (XAI) techniques to optimize CPU resource scheduling in cloud operating systems. By integrating interpretable models such as decision trees and SHAP (SHapley Additive exPlanations) values with deep learning-based schedulers, the framework not only enhances scheduling accuracy but also offers transparency in decision-making processes. Experimental results on synthetic and real-world workloads demonstrate the effectiveness of the proposed framework in improving system performance while providing human-understandable insights into scheduling logic.
DOI: 10.71465/fias281
Author: Pak Ho Leung
Institution: City University of Hong Kong
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411413764