Enhancing Operating System Performance With Ai: Optimized Scheduling and Resource Management
Year: 2025
Type: article
Abstract: This study explores AI-optimized scheduling and resource management in modern operating systems, addressing challenges related to efficiency, adaptability, and energy consumption.Traditional scheduling techniques struggle to handle dynamic workloads effectively, whereas AI-driven approaches, such as reinforcement learning and neural architecture search, enable real-time workload prediction and optimized task execution.These methods enhance CPU utilization, reduce latency, and improve power efficiency, making them well-suited for cloud computing, multi-core processing, and mobile environments.Despite challenges such as computational overhead and security risks, ongoing advancements in AI continue to refine scheduling strategies, paving the way for more adaptive and efficient next-generation operating systems.
Authors: Vasuki Shankar, Krishna B Srivatsava, Xiaoyong Li
Language: English
Topic: Real-Time Systems Scheduling
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410535677