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Key Performance Metrics in Software Design

1. Response Time:
   • Explanation: Measures the time taken for a system to respond to a request. Lower response times are crucial for user satisfaction.
   • Example: An e-commerce website’s product page should load within 2 seconds to keep users engaged.
   • Tuning Factors:
     • Code Optimization: Simplify algorithms and reduce redundant code.
     • Caching: Use a content delivery network (CDN) to cache static assets.
     • Asynchronous Processing: Load non-essential resources asynchronously to speed up initial load times.
2. Throughput:
   • Explanation: Number of transactions or processes a system can handle in a given time. Higher throughput indicates better capacity.
   • Example: A payment gateway should process thousands of transactions per second to handle peak loads.
   • Tuning Factors:
     • Load Balancing: Distribute traffic across multiple servers using a load balancer.
     • Database Optimization: Use indexing to speed up query responses.
     • Concurrency Management: Implement multi-threading to handle multiple transactions simultaneously.
3. CPU Utilization:
   • Explanation: Percentage of CPU capacity used. High CPU usage can indicate a need for optimization.
   • Example: A video streaming service should keep CPU usage under 70% to avoid performance degradation.
   • Tuning Factors:
     • Code Optimization: Optimize loops and recursive functions.
     • Concurrency Management: Properly manage threads to avoid CPU contention.
     • Load Balancing: Distribute workloads evenly across multiple CPUs.
4. Memory Usage:
   • Explanation: Amount of memory consumed by applications. Effective memory management prevents crashes.
   • Example: A mobile app should use minimal memory to avoid slowing down the device.
   • Tuning Factors:
     • Code Optimization: Use efficient data structures to reduce memory footprint.
     • Garbage Collection Tuning: Configure garbage collection for optimal memory management.
     • Memory Profiling: Identify and fix memory leaks using profiling tools.
5. Disk I/O:
   • Explanation: Speed at which data is read from or written to the disk. Essential for data-heavy applications.
   • Example: A database server should have fast read/write speeds to handle large volumes of data efficiently.
   • Tuning Factors:
     • Database Optimization: Optimize queries to reduce disk access.
     • Caching: Store frequently accessed data in memory.
     • Compression: Compress data to reduce I/O operations.
6. Network Latency:
   • Explanation: Time taken for data to travel across the network. Lower latency is crucial for real-time applications.
   • Example: A multiplayer online game needs low latency to ensure smooth gameplay.
   • Tuning Factors:
     • Caching: Cache data locally to reduce network requests.
     • Content Delivery Networks (CDNs): Use CDNs to deliver content closer to users.
     • Compression: Compress data to reduce transmission time.
7. Error Rates:
   • Explanation: Frequency of errors in the system. Lower error rates improve reliability.
   • Example: An API service should aim for an error rate below 1% to ensure high availability.
   • Tuning Factors:
     • Code Optimization: Write robust code with proper error handling.
     • Monitoring and Alerts: Use monitoring tools to detect and alert on errors.
     • Retry Logic: Implement retry mechanisms for transient errors.