https://github.com/swish78/os-assignment-algorithms-in-cpp
Implementation of Popular Operating System Algorithms
https://github.com/swish78/os-assignment-algorithms-in-cpp
algorithms cpp17 operating-system
Last synced: 9 months ago
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Implementation of Popular Operating System Algorithms
- Host: GitHub
- URL: https://github.com/swish78/os-assignment-algorithms-in-cpp
- Owner: Swish78
- License: mit
- Created: 2024-04-17T18:44:50.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-22T02:56:34.000Z (almost 2 years ago)
- Last Synced: 2025-04-16T01:50:21.687Z (11 months ago)
- Topics: algorithms, cpp17, operating-system
- Language: C++
- Homepage:
- Size: 25.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OS Assignment Algorithms in C++
Welcome to the OS Assignment Algorithms repository! This repository contains implementations of popular operating system-related algorithms in C++. The concepts and techniques used in these implementations were learned from various sources, including Gate Smasher and Jenny's Lectures in Computer Science and Information Technology.
## About
This repository serves as a resource for understanding and experimenting with fundamental operating system algorithms. Each algorithm is implemented in C++, providing clear examples of their functionality and usage.
## Included Algorithms
- Process Scheduling Algorithms
- Memory Management Algorithms
- File System Algorithms
- CPU Synchronization and Deadlock Handling
## Contributions
Contributions are welcome! If you have implemented additional algorithms or improvements to existing ones, feel free to submit a pull request.
## Resources
- [Gate Smasher](https://youtube.com/playlist?list=PLxCzCOWd7aiGz9donHRrE9I3Mwn6XdP8p&si=-eKOH2lX8Bb_3hCs)
- [Jenny's Lectures CS IT](https://youtube.com/playlist?list=PLdo5W4Nhv31a5ucW_S1K3-x6ztBRD-PNa&si=flQDXRUo2-4FvAva)
## Disclaimer
This repository is intended for educational purposes only. The implementations provided here are simplified versions for learning and experimentation and may not be suitable for production environments.