Theory

  1. Traffic Management with Load Balancers
    • Hardware Load Balancers: Specialized devices ensuring high throughput and low latency.
    • Software Load Balancers: Tools like NGINX, HAProxy, and Apache running over generic servers.
    • Cloud-Based Load Balancers: AWS ELB, Google Cloud Load Balancing for scalable traffic distribution.
    • Load Balancing Algorithms:
      • Round robin distributes requests to all servers.
      • Least Connections routes traffic to the server with the least number of connections.
      • IP Hash routes requests based on the IP address.
    • Benefits include improved response time and fault tolerance.
  2. Schedulers: Optimizing Resource Allocation
    • Schedulers allocate tasks and resources (CPUs, GPUs, memory) efficiently in distributed systems.
    • Key Schedulers:
      • Kubernetes: Assigns pods to nodes in clusters.
      • Slurm: Manages resources and job queues in HPC.
      • Apache YARN: Manages resources for big data in Hadoop.
    • Scheduling Algorithms:
      • FIFO processes tasks in the order received.
      • Fair Scheduling shares resources equally.
      • Priority-Based allocation depends on task importance.
    • Benefits include maximizing resource utilization and reducing wait times.
  3. Monitoring Performance Metrics
    • Key Metrics:
      • CPU and GPU utilization: Percentage of processing power used.
      • RAM and VRAM consumption to avoid bottlenecks.
      • Request handling efficiency.
    • Monitoring Tools:
      • Prometheus for collecting live metrics.
      • Grafana for customizable dashboards.
      • NVIDIA DCGM for GPU health and performance.
    • Real-time monitoring helps avoid bottlenecks.