Computer Architecture

1. System Scalability
   1. Definition and Importance
      1. Understanding Scalability in Computing
         1. The ability to handle growth in workload
         2. Maintaining performance with increased demand
      2. Importance of Scalability
         1. Supports business growth
         2. Enhances resource efficiency
         3. Future-proofs infrastructure
   2. Types of Scalability
      1. Vertical Scalability (Scaling Up)
         1. Increasing the power of existing hardware
            1. Upgrading CPUs
            2. Adding more RAM
            3. Replacing storage with faster alternatives
         2. Benefits and Limitations
            1. Simplicity of management
            2. Single point of failure
            3. Physical limitations
      2. Horizontal Scalability (Scaling Out)
         1. Adding more machines or nodes
            1. Load balancing across servers
            2. Clustering
         2. Benefits and Limitations
            1. Fault tolerance
            2. Complexity in management
            3. Network overhead
   3. Implementation Strategies
      1. Load Balancing
         1. Distributing workload evenly across systems
         2. Techniques
            1. Round Robin
            2. Least Connections
            3. IP Hashing
      2. Distributed Systems
         1. Breaking down services into microservices
         2. Implementing Service-Oriented Architecture (SOA)
      3. Data Distribution and Sharding
         1. Partitioning data for distributed databases
         2. Ensuring availability and consistency
   4. Challenges in Scalability
      1. Architectural Complexity
         1. Managing software dependencies
         2. Ensuring system compatibility
      2. Cost Considerations
         1. Cost of infrastructure upgrades
         2. Ongoing maintenance and support
      3. Performance Bottlenecks
         1. Identifying and mitigating bottlenecks
         2. Monitoring and optimizing resource usage
   5. Scalability in Multiprocessor Systems
      1. Fundamentals of Multiprocessor Scalability
         1. Improved computation power
         2. Effective task distribution
      2. Techniques for Scalability
         1. Parallel Computing
            1. Use of cores and threads
            2. Synchronization issues
         2. Interconnection Networks
            1. Network topology
            2. Latency and throughput considerations
      3. Scalability Challenges
         1. Memory Sharing Conflicts
            1. Cache coherence
            2. False sharing
         2. Synchronization and Deadlocks
   6. Best Practices for Ensuring Scalability
      1. Designing for Scalability from the Start
         1. Modular architecture
         2. Clean code practices
      2. Regular Testing and Benchmarking
         1. Stress testing under different loads
         2. Performance profiling
      3. Continuous Monitoring and Optimization
         1. Use of monitoring tools
         2. Real-time analytics and adjustments