Building Process Mind: An Operating System Algorithm Practice Platform
A deterministic tool for practicing and verifying Operating System algorithms.
Stack: Next.js 16 · React 19 · TypeScript · Tailwind CSS · React Hook Form · Zod
Deployment: Vercel
The Problem
While preparing for Operating System exams, I found myself repeatedly solving problems involving CPU scheduling, page replacement, disk scheduling, and memory allocation.
The difficult part wasn't always understanding the algorithm. It was checking whether my calculation was actually correct.
I wanted a simple workflow:
Enter a problem → run the algorithm → verify the result → understand where the answer came from.
AI can explain algorithms and solve problems, but for numerical algorithm problems I wanted something deterministic and reproducible.
That became Process Mind.
What Is Process Mind?
Process Mind is an interactive platform for practicing Operating System algorithms and quickly verifying solutions.
The current version covers:
| Topic | Algorithms |
|---|---|
| CPU Scheduling | FCFS, SJF, SRTF, Priority, Preemptive Priority, Round Robin |
| Page Replacement | FIFO, LRU, Optimal, Second Chance, LRU Counter |
| Disk Scheduling | FCFS, SSTF, SCAN, C-SCAN, LOOK, C-LOOK |
| Memory Allocation | First Fit, Best Fit, Worst Fit, Next Fit |
The project is intentionally being developed incrementally rather than trying to implement every possible algorithm at once.
Deterministic by Design
The core idea behind Process Mind is simple:
The same input should produce the same result every time.
For example, a CPU scheduling problem can produce:
- Completion Time
- Turnaround Time
- Waiting Time
- Response Time
- Average metrics
- Gantt Chart
- Execution Steps
This makes the platform useful for exam preparation because I can compare my manually calculated answer against a reproducible implementation.
Process Mind isn't intended to replace AI. It focuses on a narrower problem: calculation, verification, and understanding algorithm execution.
The Interesting Part Wasn't Writing the Algorithms
Implementing FCFS or SJF is relatively straightforward.
The harder question was:
What information should the application expose so that someone can actually understand and verify the result?
A final answer isn't enough.
For CPU scheduling, Process Mind represents information such as:
- current execution time
- selected process
- ready processes
- waiting queue
- processes arriving during execution
- queue after execution
- execution window
- reason for selecting a process
This allows the application to show not only what happened, but also why it happened.
From Input to Result
I structured the scheduling system around a common result model:
User Input
↓
Validation
↓
Algorithm
↓
Structured Result
├── Metrics
├── Gantt Blocks
├── Process Details
└── Execution Steps
↓
UI
Each algorithm has its own implementation, but they return a consistent result structure. This allows the UI to remain largely independent of the underlying scheduling logic.
The algorithm configuration also determines requirements specific to each algorithm. For example, Priority Scheduling needs a priority field, while Round Robin needs a time quantum.
Handling Preemptive Algorithms
Preemptive scheduling introduced another challenge.
Algorithms such as SRTF and Preemptive Priority Scheduling can interrupt a process and resume it later. Because of this, the system needs to track remaining execution time, not just whether a process has completed.
The resulting execution state is then used to generate Gantt blocks and execution steps.
This approach keeps the visualization derived from the algorithm's actual state rather than building the visualization separately from the calculation.
Metrics, Validation, and Practice
A simulator is only useful if its inputs and outputs are reliable.
Process Mind validates values such as:
- non-negative arrival times
- positive burst times
- valid priorities
- positive Round Robin quantum
- unique process IDs
The forms use React Hook Form and Zod for validation.
For practice, the platform can also generate random process inputs with configurable arrival, burst, and priority values.
The intended workflow becomes:
Generate → Solve → Verify → Repeat
What I Learned
The biggest lesson from Process Mind was that implementing an algorithm is only one part of building an algorithm application.
The real engineering work was deciding how to represent:
algorithm logic → intermediate state → metrics → explanation → UI
I also learned not to over-engineer future requirements. Different Operating System algorithms have different data and execution models, so I chose to share interfaces and infrastructure where it makes sense while keeping individual algorithm implementations independent.
What's Next?
Process Mind is still evolving.
The current focus is on improving the existing algorithms and practice experience before expanding into more sophisticated simulations or additional Computer Science topics.
Future development will be driven largely by actual usage and feedback rather than adding features simply to increase the feature list.
Final Thoughts
Process Mind started with a simple problem:
I wanted a faster way to practice Operating System algorithms and verify my answers.
Building it showed me that the interesting challenge isn't just calculating the answer.
It's turning the calculation into something a person can inspect, understand, and trust.
Input → Algorithm → State → Metrics → Verification
That's the idea behind Process Mind.