Cloud Compute Diagnostics Lab

Overview

This project simulates a cloud engineering diagnostic system used to validate compute operations and ensure instruction accuracy within a processing environment.

It combines low-level compute unit planning with a structured diagnostics runner that tests, validates, and reports on system behavior.

The original project focused on ALU design documentation. This upgraded version positions the work as a compute diagnostics lab that connects digital logic, validation testing, and reliability-focused engineering workflows.

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Diagnostics Report Preview

The screenshot below shows the diagnostics runner validating compute operations, comparing expected versus actual outputs, and generating a structured report.

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Project Objective

To simulate how cloud engineers validate system reliability by:

• Executing Controlled Instruction Test Cases
• Comparing Expected Versus Actual Outputs
• Identifying Inconsistencies In Compute Logic
• Generating Structured Diagnostic Reports
• Supporting Root Cause Analysis For Incorrect Outputs

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Simulated Environment

• Compute Unit Executing Arithmetic And Logic Operations
• Input-Driven Instruction Testing
• Diagnostics System Validating Execution Results
• JSON-Based Test Case Definitions
• Report Generation For System Analysis

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System Architecture

Compute Layer
        |
        v
ALU Design Documentation And Compute Operation Definitions
        |
        v
Diagnostics Layer
        |
        v
Python Script Running Validation Tests
        |
        v
Data Layer
        |
        v
JSON-Based Test Case Definitions
        |
        v
Reporting Layer
        |
        v
Generated Diagnostics Report Summarizing Results

This structure reflects how engineering teams separate compute logic, test data, validation execution, and reporting when checking system reliability.

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Scenario

In cloud systems, incorrect instruction execution can cause:

• Data Corruption
• Unexpected Application Behavior
• System Instability
• Failed Downstream Processing

This project simulates detecting and diagnosing these issues before deployment by validating compute operations against known expected results.

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Diagnostic Workflow

• Load Predefined Test Cases
• Execute Simulated Compute Operations
• Compare Expected Versus Actual Outputs
• Identify Failed Test Cases
• Generate A Structured Diagnostics Report
• Save The Report For Review And Escalation

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Test Cases

Case ID	Operation	Input A	Input B	Expected	Actual	Status
CPU-001	ADD	5	3	8	8	Pass
CPU-002	SUB	10	4	6	6	Pass
CPU-003	AND	5	3	1	1	Pass
CPU-004	OR	5	2	7	7	Pass
CPU-005	XOR	6	3	5	5	Pass

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Implementation

• Designed The Project Around ALU Compute Validation
• Created JSON-Based Test Cases For Validation Scenarios
• Developed A Python Diagnostics Runner To Execute And Verify Results
• Implemented Report Generation To Summarize System Behavior
• Preserved Existing ALU Documentation As Architecture Reference Material

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Diagnostics Output

Example report:

Cloud Compute Diagnostics Report
========================================

Total Tests: 5
Passed: 5
Failed: 0

CPU-001 | ADD | Input A: 5 | Input B: 3 | Expected: 8 | Actual: 8 | PASS
CPU-002 | SUB | Input A: 10 | Input B: 4 | Expected: 6 | Actual: 6 | PASS
CPU-003 | AND | Input A: 5 | Input B: 3 | Expected: 1 | Actual: 1 | PASS

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Repository Structure

Cloud-Compute-Diagnostics-Lab/
|-- logisim/
|   |-- README.md
|   |-- cloud_compute_unit.circ
|
|-- reports/
|   |-- diagnostics_report.txt
|
|-- screenshots/
|   |-- diagnostics-report.png
|
|-- diagnostics_runner.py
|-- test_cases.json
|-- README.md
|-- ALU_Instruction_Set.md
|-- ALU_Architecture_Notes.md
|-- ALU_Test_Plan.md
|-- Basic ALU.docx

Note: the verified Logisim .circ file and circuit screenshots should be added after the ALU circuit is exported from Logisim.

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Technologies Used

• Logisim For Digital Logic Simulation
• Python For Diagnostics And Validation
• JSON For Test Case Definitions
• Text Reports For Diagnostic Output
• ALU Design Documentation For Compute Logic Reference

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How To Run

Run diagnostics:

python diagnostics_runner.py

View output:

reports/diagnostics_report.txt

When the verified Logisim circuit is available, place it here:

logisim/cloud_compute_unit.circ

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Planned Enhancements

• Introduce Automated Failure Detection Alerts
• Expand Test Coverage Across More Instruction Types
• Simulate Multi-Step Instruction Pipelines
• Add Visualization Dashboard For Diagnostics
• Add Verified Logisim Circuit Export
• Add ALU Circuit Screenshots
• Add Failing Test Case Examples For Troubleshooting Practice

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Real-World Relevance

This project reflects cloud engineering and support practices:

• Validating System Behavior Before Deployment
• Detecting Low-Level Computation Errors
• Supporting Root Cause Analysis For Incorrect Outputs
• Ensuring System Reliability And Consistency
• Creating Repeatable Diagnostic Reports
• Separating Test Data, Execution Logic, And Reporting Output

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Professional Positioning

Before, this project showed ALU planning.

Now, it demonstrates a diagnostics system that validates compute reliability and generates reports.

That is a stronger portfolio story because it connects low-level computer architecture knowledge to reliability testing, validation workflows, and operational diagnostics.