OS Scheduling Visualizer

A simple educational desktop app (Tkinter + Matplotlib) to visualize and compare CPU scheduling algorithms for an Operating Systems course. The app lets you input processes, configure context-switch time and round-robin quantum, run multiple scheduling algorithms, and inspect zoomable Gantt charts and per-process statistics.

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Features

• GUI built with Tkinter (ttk) and Matplotlib.

• Algorithms implemented:

  • FCFS (First-Come, First-Served)
  • SJF (Shortest Job First) — non-preemptive
  • SRTF (Shortest Remaining Time First) — preemptive
  • Priority (non-preemptive)
  • Priority (preemptive)
  • Round Robin (configurable quantum)

• Context switch time properly applied and cumulatively shifts later segments.

• Per-algorithm tabs with:

  • Table of per-process WT (Waiting Time), TAT (Turnaround Time), completion time
  • A MEAN row with Mean WT and Mean TAT
  • Zoomable/pannable Gantt chart (Matplotlib toolbar)
  • A legend showing a unique color for each process

• Comparison tab showing mean WT and mean TAT for all run algorithms

• Scrollable UI (works on small screens) and styled controls for better readability

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Prerequisites

• Python 3.8+ (3.10 recommended)
• conda (recommended) or mamba (optional)

Required Python packages:

• matplotlib
• tk/tcl (for tkinter) — provided by the system or installable via conda

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Setup (recommended: conda)

Open an Anaconda/Miniconda shell and run:

# Create and activate environment (uses conda-forge for reliable tk/matplotlib packages)
conda create -n os_scheduler python=3.10 matplotlib tk -c conda-forge -y
conda activate os_scheduler

# (Optional) if matplotlib missing after env creation:
# pip install matplotlib

Or with mamba (faster resolver):

mamba create -n os_scheduler python=3.10 matplotlib tk -c conda-forge -y
mamba activate os_scheduler

If you prefer system Python + pip (not recommended for Windows without tkinter):

python -m pip install matplotlib
# Ensure tkinter is available (usually preinstalled on many Linux/macOS distros)

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Files

• appV4.py — main application file (or the Python filename you saved from this project).
• README.md — this file.
• appV#.py — other versions of the application

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How to run

From the activated environment, run:

python appV4.py

If you use a different name for the file (e.g. appV3.py), run that instead:

python appV3.py

Fullscreen and shortcuts

• The app supports toggling fullscreen using F11 and exiting fullscreen with Esc (if that feature was enabled in your build).
• You can also maximize the window manually.

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Quick usage guide

1. Add processes: Enter Arrival, Burst, and Priority, then click Add Process. Repeat for all processes.
2. Set parameters: Enter Context Switch time and Time Quantum (RR) if using Round Robin.
3. Select algorithms: Choose which algorithms to run using the checkboxes.
4. Run: Click Run Selected.
5. Inspect results: Each algorithm appears in its own tab. The table contains per-process WT/TAT/CT and a MEAN row. The Gantt chart is below the table and can be zoomed/panned using the Matplotlib toolbar (zoom, pan, reset, save).
6. Comparison: Open the Comparison tab to see mean WT & TAT across algorithms.

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Notes about context switching

• Context switch time is inserted each time the scheduler switches from one PID to another when the CPU is busy (and when a switch occurs exactly at the previous end time). The algorithm preserves idle gaps — a context switch is not forcibly inserted earlier than a process start if the CPU was idle in between.
• The insert_context_switch routine shifts subsequent schedule segments when context switch time is added so the timeline remains correct.

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UI / Styling

• Uses ttk (clam theme) and custom styles to make buttons and tables visually clearer.
• Each process gets a distinct color (from Matplotlib's tab20 palette). The color legend appears in each algorithm tab.
• Charts are placed under each algorithm table to improve reading flow.

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Troubleshooting

• Matplotlib toolbar errors (TclError about pack/grid): Ensure the version of the app uses a separate frame for the toolbar (this code does). If you see errors, update to the most recent version of the script where toolbar frames are created separately.
• Tkinter missing on Windows: Use the conda command above (tk -c conda-forge) to install a working tkinter.
• Charts clipped or too small: Use the Matplotlib toolbar's Zoom tool and Pan to inspect. The UI is scrollable if your screen is small.

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Extending the app

Ideas you might want to add:

• Save/export per-algorithm CSV of stats.
• Export Gantt charts as PNG/SVG via the toolbar (already supported by Matplotlib's Save tool).
• Pre-defined scenario presets for lecture demos.
• Dark theme / ttkbootstrap integration.
• Edit processes inline in the table.

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License

This project is provided as-is for educational purposes. Feel free to reuse and adapt for teaching. No warranty.