AI-Driven CPU Scheduling and Process OptimizationAI-Driven CPU Scheduling and Process Optimization
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Ayush Agrawal
• 4h
AI CPU Scheduler | Intelligent Process Scheduling
Developed an AI-driven CPU scheduling project that explores the use of machine learning techniques to improve process scheduling decisions. The system combines operating-system scheduling concepts with intelligent decision-making to evaluate process workloads and optimize scheduling performance.
Key Features
Intelligent Scheduling: Applies AI-based decision-making to process scheduling.
Process Management: Works with process characteristics such as arrival time, burst time, and scheduling requirements.
Scheduling Optimization: Aims to improve CPU utilization and reduce process waiting and turnaround times.
Performance Evaluation: Supports evaluating scheduling decisions using relevant process-scheduling metrics.
Algorithm Comparison: Provides a foundation for comparing AI-driven scheduling with traditional scheduling approaches.
Operating Systems Concepts: Demonstrates process execution, scheduling policies, and resource allocation.
Tech Stack: Python and AI/ML techniques, subject to confirmation against the repository.
Objective: Explore how intelligent scheduling can improve resource utilization, process execution efficiency, and overall scheduling performance.
AI Engineer Machine Learning EngineeringPythonTensorFlow
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