Building an MLFQ Scheduler Simulator with Python

Operating systems must constantly decide which processes run next, balancing speed and fairness. Understanding how these complex scheduling algorithms work under the hood can seem daunting, but building your own simulation makes these abstract concepts concrete. By reading through this guide, you will transition from understanding basic scheduling theory to implementing a fully functional Multi-Level Feedback Queue (MLFQ) simulator in Python. You will learn how operating systems prioritize interactive tasks, prevent starvation, and dynamically adjust job priorities based on execution behavior. What you'll learn: - Understand the core principles of process scheduling, queue management, and priority dynamics in modern operating systems. - Model scheduling entities using modern Python features like dataclasses and type hints for clean, robust code. - Implement the core mechanics of a Multi-Level Feedback Queue, including time slices, priority demotion, and periodic priority boosts. - Configure custom simulator parameters to analyze how different workloads affect CPU utilization and response times. - Analyze simulation outputs to observe how interactive and CPU-bound processes behave under varying scheduler policies. The course begins with foundational operating system concepts, defining terms like starvation, context switching, and time quantum. You will then progress step-by-step through designing the simulation logic, writing the Python code, and running test scenarios. This course is designed for beginner programmers, computer science students, and aspiring systems engineers who want a hands-on introduction to operating system internals. No prior knowledge of OS theory is required, though basic Python familiarity is helpful. Start reading today to demystify process scheduling and build your own functional simulator.