System Scalability and Performance

• 1: Scalability Architecture
• 1.1: Modular System Design
• 1.2: Cloud Infrastructure Utilization
• 1.3: Resource-Limited Deployment
• 2: Performance Optimization
• 2.1: Load Balancing Techniques
• 2.2: Performance Monitoring Tools
• 2.3: Resource Optimization Metrics

1 - Scalability Architecture

1.1 - Modular System Design

• Independent Microservices: Separate data acquisition, analysis, and reporting modules.
• Autonomous Updates: Patch each module without bringing down the entire system.
• Flexible Deployment: Support a small single-class pilot or a massive cloud-based rollout.

1.2 - Cloud Infrastructure Utilization

• Dynamic Scaling: Ramp up computing power during high usage.
• Distributed Systems: Mirror data across different regions for load balancing and failover.
• Cost Optimization: Align resource allocation with actual demand cycles.

1.3 - Resource-Limited Deployment

• Offline Modes: Retain local caches in areas of unstable connectivity.
• Local Servers: Handle essential tasks or buffer data locally.
• Minimal Hardware Requirements: Ensure compatibility with budget devices.

2 - Performance Optimization

2.1 - Load Balancing Techniques

• Traffic Distribution: Route data requests evenly among multiple servers.
• Failover Strategies: Keep a backup server on standby.
• Auto-Scaling: Match capacity with fluctuations over the academic calendar.

2.2 - Performance Monitoring Tools

Real-Time Metrics: Track CPU, memory, and network usage.

• Alerts: Flag abnormal spikes or latencies.
• Reporting: Generate periodic performance summaries for system administrators.

2.3 - Resource Optimization Metrics

Cost Efficiency: Correlate hosting or data center expenses with crucial system metrics.

• Adaptive Provisioning: Analyze historical usage to forecast future needs.
• Historical Data: Use archived performance metrics to refine resource planning.