Technology Networking

Edge Computing Networking: A Beginner's Guide to Architectures, Connectivity, and Best Practices

Updated on
7 min read

Edge computing revolutionizes how we process and store data by moving services closer to users and devices, rather than relying solely on centralized cloud computing. This article focuses on edge computing networking—exploring the connections, protocols, and architectures that enable efficient, low-latency applications. Whether you’re a developer, a network engineer, or a business leader interested in IoT, AR/VR, or industrial automation, this guide will provide you with a comprehensive overview of the core concepts, design considerations, and practical steps for implementing edge networks.

Core Concepts and Terms (Beginner-Friendly)

  • Edge Node / Edge Device / Micro Data Center

    • Edge Device: Endpoints like sensors, cameras, or vehicles that generate or use data.
    • Edge Node: A host that runs applications at the edge (e.g., gateways, mini servers).
    • Micro Data Center: Small, rackable data centers deployed near users.

  • Latency, Jitter, Throughput, Bandwidth

    • Latency: The time taken for a packet’s round trip—crucial for interactive applications.
    • Jitter: Variation in latency over time, which can disrupt real-time audio, video, and controls.
    • Bandwidth vs Throughput: Available capacity versus actual transfer rates.

  • Cloud-Edge Continuum and Fog Computing

    • Continuum: A spectrum from constrained devices to gateways, edge nodes, and the public cloud.
    • Fog Computing: A hierarchical distribution of compute/storage across this continuum.

  • Control Plane vs Data Plane

    • Data Plane: Handles user and application traffic.
    • Control Plane: Manages configuration, orchestration, and routing.

  • Orchestration, Telemetry, and Observability

    • Orchestration: Automates deployment and management across edge nodes.
    • Telemetry: Collects logs, metrics, and traces to monitor system health.
    • Observability: Converts telemetry into actionable insights.

For deeper academic insights, refer to the survey paper by Satyanarayanan et al. (link in references).

Common Edge Networking Architectures

  • Multi-Access Edge Computing (MEC)

    • A telco-focused architecture that provides cloud-like computing at mobile base stations and facilitates low-latency APIs. Ideal for 5G applications.

  • Fog Computing

    • Hierarchical architecture with devices, gateways, and regional edge nodes to optimize data processing and reduce latency.

  • Micro Data Centers and On-Prem Edge Sites

    • Compact racks located within enterprises for enhanced privacy and control over data processing.

  • Hybrid and Multi-Cloud Edge Patterns

    • Combines edge computing for low latency with the cloud for intensive processing tasks.

(For MEC standards and APIs, visit the ETSI MEC documentation).

Network Technologies & Protocols Used at the Edge

Access Technologies

  • 5G: Delivers high bandwidth and low latency—crucial for mobile edge applications.
  • Wi-Fi 6 / 6E: Provides high-throughput local access.
  • Ethernet: A reliable backbone for connecting edge servers and gateways.

WAN / Backhaul

  • SD-WAN: Simplifies secure routing across diverse edge sites.
  • Private LTE/5G and Fiber: Ensure reliable backhaul with predictable latency.

Application Protocols

  • MQTT, CoAP: Lightweight protocols suitable for IoT devices.
  • HTTP/2, HTTP/3, and QUIC: Optimized for lower overhead and improved resilience.

Transport Layer Considerations

  • TCP vs UDP: TCP is reliable but may introduce delays; UDP is preferred for low-latency demands.

Network Functions and Virtualization

  • NFV: Enables deployment of routing, firewall, and gateway functions as virtual machines at the edge.

Design Considerations for Edge Networks

  • Latency Budgeting
    Allocate round-trip time effectively across processing and network hops to meet latency goals.

  • Bandwidth and Data Reduction Strategies
    Implement local data filtering and inference to minimize upstream loads.

  • Reliability and High Availability
    Design for local failover and use redundant paths to enhance availability.

  • Intermittent Connectivity Resilience
    Build offline-first applications to handle connectivity issues gracefully.

  • Data Governance and Sovereignty
    Safeguard sensitive data locally for compliance with regulations.

Deployment Patterns and Edge Service Models

  • Device-Edge, Gateway-Edge, Cloud-Assisted Edge
    Each model optimizes processing based on the required computational load and latency needs.

  • MEC vs CDN Edge
    MEC provides integrated compute capabilities, while CDN focuses on caching and content delivery.

  • Push vs Pull Updates
    Determine update strategies based on network resilience and orchestration needs.

  • Containers and Lightweight VMs
    Leverage containers for agility and lightweight VMs for stronger isolation.

For insights into container networking, see our Container Networking — Beginners Guide.

Connectivity & Infrastructure Essentials

Key Infrastructure Components

  • Edge servers, gateways, access switches, and power backup are crucial for effective edge operations.

Last-Mile Considerations

  • Plan for potential bottlenecks in the last mile and allocate resources for peak demand.

Cost Tradeoffs

  • Evaluate options between colocation, on-premise, and cloud provider solutions to optimize total cost of ownership.

For hardware setup guidance for an edge lab, refer to our Building a Home Lab (Hardware Requirements).

Security and Privacy for Edge Networks

Unique Threat Models

  • Understand the increased attack surface presented by distributed edge endpoints.

Best Practices

  • Implement strong network segmentation, authentication, and secure update mechanisms.

Operational Security Planning

  • Incorporate key rotation, secure boot practices, and monitoring strategies for compromised devices.

Management, Orchestration, and Observability

The Need for Orchestration

  • Employ lightweight Kubernetes distributions for efficient management of numerous nodes.

CI/CD and Update Patterns

  • Utilize strategies like canary rollouts and blue/green deployments for safer updates in remote locations.

Remote Troubleshooting

  • Develop secure remote access methodologies to facilitate effective maintenance of edge devices.

Typical Use Cases and Real-World Examples

  • IoT Telemetry: Gateways filter and preprocess sensor data for cloud analytics.
  • AR/VR and Gaming: Reduced latency enhances user experience in immersive applications.
  • Industrial Automation: Ensures safety and operational continuity through local control.
  • Autonomous Vehicles: Utilize local computations for real-time decision-making.
  • Content Delivery: Edge caches improve streaming performance and reduce origin server load.

Getting Started — Practical Steps for Beginners

Start your edge computing journey with a simple lab experiment. Here’s a one-day lab setup you can try:

  1. Define your latency goal (e.g., less than 100 ms).
  2. Choose your hardware: Consider a Raspberry Pi 4 or Intel NUC.
  3. Install K3s on your device: 
    curl -sfL https://get.k3s.io | sh -
sudo k3s kubectl get nodes
  4. Run an MQTT broker (Eclipse Mosquitto) for message publishing and subscribing: 
    sudo apt update && sudo apt install mosquitto mosquitto-clients -y
mosquitto_sub -h localhost -t sensors/temperature
mosquitto_pub -h localhost -t sensors/temperature -m "{\"temp\": 22.5}"
  5. Simulate network latency with tc/netem: 
    sudo tc qdisc add dev eth0 root netem delay 50ms loss 1%
sudo tc qdisc del dev eth0 root netem
  6. Develop a simple Python subscriber to process data locally (placeholder code provided).
  7. Measure latency to compare edge versus cloud processing.

Useful tools include K3s for orchestration, Eclipse Mosquitto for MQTT, and tc/netem for network simulation. To dive deeper on container networking or Windows integration, explore our guides linked above.

Current Challenges

  • Navigating operational complexity across numerous remote sites can be challenging and costly.
  • Addressing interoperability amidst fragmented vendor solutions is essential for seamless edge functionalities.
  • Expect advancements in machine learning at the edge and tighter integration with 5G, enhancing real-time processing capabilities.

Conclusion

Networking significantly influences edge computing architectures, affecting performance and reliability. Begin by setting up a small lab to understand the benefits of processing at the edge. As you progress, iterate towards more complex solutions, incorporating orchestration, secure updates, and observability.

For further reading and resources, explore the following:

Additional resources include:

Ready to dive into edge computing? Start with the suggested lab and observe the latency benefits of edge processing.

TBO Editorial

About the Author

TBO Editorial writes about the latest updates about products and services related to Technology, Business, Finance & Lifestyle. Do get in touch if you want to share any useful article with our community.