Edge & Fog Computing

Introduction to Edge & Fog Computing

Part 5.3 of Module 5: System Design

This module explores edge computing, fog computing, and distributed intelligence paradigms that bring computation closer to data sources.

Overview

Modern IoT systems increasingly leverage edge and fog computing to reduce latency, improve reliability, and optimize bandwidth usage. This module covers:

  • Edge Computing Fundamentals - Edge architectures, device capabilities, and deployment models
  • Fog Computing Architecture - Multi-tier fog layers, cloudlets, and orchestration
  • Edge AI/ML - TinyML, model optimization, on-device inference
  • Decision Frameworks - When to use edge vs. fog vs. cloud
  • Performance Optimization - Latency reduction, resource allocation, network selection
  • Use Cases & Applications - Real-world deployments and best practices

Key Topics

47 Chapters Covering
  • Edge-Fog continuum architecture
  • Compute placement strategies
  • Real-time processing at the edge
  • Edge AI/ML hardware and frameworks
  • Network bandwidth optimization
  • Quality of Service (QoS) management
  • Production deployment patterns

Learning Path

  1. Fundamentals - Start with edge-fog basics and architecture concepts
  2. Deep Dive - Explore edge AI/ML and optimization techniques
  3. Applications - Study real-world use cases and deployment strategies
  4. Production - Learn best practices for production systems

Module Color: Goldenrod #B8860B | Part: 5.3 | Chapters: 47

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