AOS-Assessment - Guide to go through

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Repository Structure

AOS-Assessment/
│
├── task1_iot_monitor.sh        # Task 1 — IoT & System Resource Monitor
├── task2_job_scheduler.sh      # Task 2 — Priority-based Job Scheduler
│
├── Task_3/                             # Task 3 — User Authentication & Submission System
│   ├── submissions/                    # Submitted documents (act.docx, test1.docx)
│   ├── accounts_status.json            # JSON store for user account states
│   ├── login_log.txt                   # Log of all authentication attempts
│   ├── submission_log.txt              # Log of all submission events
│   ├── task3_auth.py                   # Python — user authentication module
│   └── task3_submission.sh             # Bash — submission handling script
│
├── ArchiveLogs/                        # Archived historical log files
├── test_logs/                  # Log outputs generated during testing
│
├── job_queue.txt               # Input queue file for the job scheduler
├── completed_jobs.txt          # Record of successfully completed jobs
├── scheduler_log.txt           # Runtime log produced by Task 2
└── system_monitor_log.txt      # Runtime log produced by Task 1

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Tasks

Task 1 — IoT & System Resource Monitor (task1_iot_monitor.sh)

A Bash script that continuously monitors system resources in the context of an IoT environment. It captures and logs key metrics such as CPU usage, memory consumption, disk utilisation, and running process states. All monitoring events are written to system_monitor_log.txt with timestamps, enabling historical analysis and threshold-based alerting.

Key OS concepts covered:

• Process monitoring and system calls
• Resource usage tracking (/proc filesystem interaction)
• File-based logging with timestamps
• Signal handling and daemon-style execution

Usage:

chmod +x task1_iot_monitor.sh
./task1_iot_monitor.sh

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Task 2 — Job Scheduler (task2_job_scheduler.sh)

A Bash-based job scheduling system that reads jobs from a queue file (job_queue.txt), executes them according to defined criteria, and records outcomes. Completed jobs are appended to completed_jobs.txt, while the full execution trace is maintained in scheduler_log.txt.

Key OS concepts covered:

• Job queuing and batch processing
• Process scheduling strategies
• Inter-process communication via shared files
• Background process management with &, wait, and jobs

Usage:

chmod +x task2_job_scheduler.sh
./task2_job_scheduler.sh

Job queue format (job_queue.txt): Each line represents a job entry to be scheduled and processed by the script.

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Task 3 (Task_3/)

The third assessed task is contained within the Task_3/ directory. Refer to the source files and any inline comments within that folder for implementation details.

A hybrid Python + Bash implementation of a user authentication and file submission management system. The task combines a Python authentication module with a Bash submission pipeline, demonstrating cross-language OS-level scripting and persistent state management via JSON and log files.

Files:

    File	                        Role

task3_auth.py :- Python script handling user login, credential verification, and account state management task3_submission.sh :- Bash script that manages the submission workflow - validating, processing, and logging file submissions accounts_status.json:- JSON data store tracking the status of each user account (active, locked, etc.) login_log.txt :- Timestamped log of every authentication attempt (success and failure) submission_log.txt :- Timestamped log of all submission events processed by task3_submission.sh submissions/ :- Directory holding submitted documents (act.docx, test1.docx)

Key OS concepts covered:

User authentication and access control logic Persistent state management using structured JSON files Cross-language scripting (Python + Bash interoperability) File I/O and audit logging

Run the authentication module

python3 Task_3/task3_auth.py

Run the submission handler

chmod +x Task_3/task3_submission.sh ./Task_3/task3_submission.sh

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Log Files

Log Files File Description system_monitor_log.txt :- Output log from Task 1; records resource metrics with timestamps scheduler_log.txt :- Runtime execution trace generated by Task 2's scheduler completed_jobs.txt :- Persistent record of all jobs that were successfully executed job_queue.txt :- Input queue consumed by the job scheduler Task_3/login_log.txt :- Log of all authentication attempts from Task 3 Task_3/submission_log.txt :- Log of all file submission events from Task 3 Task_3/accounts_status.json :- Persistent JSON store for user account states ArchiveLogs/ :- Historical log archives from previous test runs test_logs/ :- Logs produced during development and testing phases

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Prerequisites

-Prerequisites -Linux-based operating system (Ubuntu recommended) -Bash shell (bash --version ≥ 4.x) -Python 3 (python3 --version) - required for Task 3 -Standard GNU coreutils (awk, grep, top, df, free, ps) -Execute permissions on the scripts

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Running the Scripts

Clone the repository

git clone https://github.com/onadith-thecoder/AOS-Assessment.git cd AOS-Assessment

Make scripts executable

chmod +x task1_iot_monitor.sh task2_job_scheduler.sh Task_3/task3_submission.sh

Run Task 1 — IoT Monitor

./task1_iot_monitor.sh

Run Task 2 — Job Scheduler

./task2_job_scheduler.sh

Run Task 3 — Authentication module

python3 Task_3/task3_auth.py

Run Task 3 — Submission handler

./Task_3/task3_submission.sh

Note: Depending on your system configuration, some monitoring commands may require elevated privileges (sudo).

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Author

Venuka Onadith B.Eng (Hons) Software Engineering Undergraduate - CCCU via Saegis Campus GitHub · Portfolio