Infrastructure

Edge Computing

Edge computing moves data processing from the central data centre to the edge of the network – closer to the devices and users that produce the data.

In an increasingly connected world with billions of IoT devices, central cloud data centres reach their limits. Edge computing addresses this by processing data where it is generated – at the network edge. The result: much lower latency, less bandwidth use and the ability to react in real time even without a stable internet connection. For businesses, edge computing opens new applications from autonomous driving and industrial automation to smart retail systems.

What is Edge Computing?

Edge computing is a distributed IT architecture model in which data processing, analysis and storage happen at or near the data source – instead of only in a central cloud data centre. The 'edge' is the physical location where IoT sensors, machines, cameras or devices produce data. Edge nodes can be small servers, gateways, industrial PCs or specialized hardware like NVIDIA Jetson. Processing is local; only aggregated, filtered or especially relevant data is sent to the cloud. Edge computing complements rather than replaces cloud: the architecture forms a hierarchy of edge devices, regional edge servers and central cloud – often called the edge–cloud continuum.

How does Edge Computing work?

Edge devices collect raw data from sensors, cameras or machines and process it locally in real time with edge software or AI models (edge AI). An edge gateway aggregates data from multiple devices, does pre-processing and decides what stays local and what goes to the cloud. Containerized apps (e.g. via Kubernetes/K3s) allow consistent deployment on edge nodes. The central cloud handles model training, long-term storage and cross-cutting analytics. Orchestration platforms like AWS IoT Greengrass, Azure IoT Edge or Google Distributed Cloud manage thousands of edge nodes centrally.

Practical Examples

Autonomous driving: Vehicles process camera, lidar and radar data locally in milliseconds – seconds of latency to the cloud would be dangerous at 130 km/h.

Smart factory: Industrial sensors on production lines detect anomalies in real time and stop machines before defective parts are made (predictive maintenance).

Retail stores: Edge servers in stores process till data, customer flow (computer vision) and digital price tags even when the internet is down.

Telemedicine: Medical wearables analyse vital signs locally and alert immediately on critical values – without going through a remote data centre.

Content delivery: CDN edge servers cache videos and web content near the user for streaming without buffering.

Typical Use Cases

IoT and Industry 4.0: Real-time processing of sensor data in production with thousands of connected devices

Autonomous systems: Self-driving vehicles, drones and robots need immediate local decision-making

Video analysis: Real-time object detection and quality control with computer vision at the camera

Gaming and AR/VR: Low-latency rendering and streaming via 5G edge servers

Remote sites: Oil rigs, wind farms or construction sites with limited connectivity process data locally

Advantages and Disadvantages

Advantages

Minimal latency: Local processing in milliseconds instead of round-trip to cloud (50–200 ms saved)
Bandwidth savings: Only relevant data is sent to the cloud – up to 90% less traffic
Offline capability: Edge devices keep working during network outages
Data privacy: Sensitive data does not leave the site – good for GDPR compliance
Scalability: Thousands of edge nodes distribute load instead of overloading one data centre

Disadvantages

Complexity: Managing and orchestrating distributed edge nodes is harder than central cloud
Security: Every edge node is a potential attack surface – physical security and encryption are essential
Cost: Hardware, maintenance and staff at distributed sites can increase initial cost
Limited resources: Edge devices have less compute and storage than cloud – complex models must be optimized

Frequently Asked Questions about Edge Computing

What is the difference between edge computing and cloud computing?

Cloud computing centralizes processing in large data centres – ideal for compute-heavy tasks, big data and long-term storage. Edge computing moves some processing to the network edge for real-time and low latency. In practice both complement each other: edge handles time-critical data locally, the cloud handles training, archiving and cross-cutting analytics.

Do I need edge computing for my business?

Edge computing pays off when your application needs low latency (e.g. machine control, video analysis), large data volumes that cannot all be sent to the cloud, or your sites have unreliable internet. For classic web and SaaS products, cloud computing is usually enough.

What role does 5G play in edge computing?

5G and edge computing complement each other: 5G provides high bandwidth and low latency between devices and edge servers. Multi-Access Edge Computing (MEC) puts compute in 5G base stations for under-10 ms latency, enabling cloud gaming, AR navigation and real-time robot control.

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