Cloud Metropolis: AI-Powered Resource Orchestration

Cloud Metropolis draws inspiration from the dystopian automation of Metropolis and the resource management challenges faced in Hyperion. It's a low-cost cloud resource orchestration platform targeted at small businesses struggling with unpredictable workloads and cloud costs. The project utilizes an AI model, trained on historical resource usage data (CPU, memory, network I/O, etc.) and external factors (e.g., website traffic, seasonality) scraped from sources analogous to the 'AI Workflow for Companies' scraper. This model predicts future resource needs with high accuracy. Based on these predictions, Cloud Metropolis automatically scales cloud resources (VM instances, database capacity, etc.) up or down, minimizing costs and ensuring optimal performance. Users can define cost-saving strategies (e.g., prioritize cost over performance during off-peak hours, use spot instances when available), giving them fine-grained control. The 'Hyperion' influence comes from its themes of prediction and adaptation; the AI attempts to foresee resource contention and preemptively allocate resources to avoid performance bottlenecks, much like anticipating a pilgrimage to the Time Tombs. Key features include: 1) Historical Data Analysis: Scrape and analyze past resource usage. 2) Predictive Modeling: Utilize a time-series forecasting model (e.g., LSTM, Prophet) to predict future needs. 3) Automated Scaling: Automatically adjust cloud resources based on the predictions. 4) Cost Optimization: Implement cost-saving strategies. 5) Intuitive Dashboard: Provide a user-friendly interface for monitoring and managing resources. 6) Integration with popular cloud providers: AWS, Azure, GCP. This project is niche because it focuses specifically on small businesses with limited IT resources, low-cost because it leverages open-source AI tools and serverless cloud functions, and has high earning potential through a SaaS subscription model (freemium or tiered pricing) based on the number of monitored resources or AI predictions per month. The architectural pattern is a microservice-based architecture, offering flexibility and scalability.