Edge Computing
The term “Edge computing” refers to computing as a distributed paradigm. It is a type of distributed architecture in which data processing occurs close to the source of data, at the “edge” of the system.
It means running fewer processes in the cloud and moving those processes to local places, such as on a user’s computer, an IoT device, or an edge server.
Edge Computing vs Cloud Computing
- Cloud is centralized & Edge is decentralized.
- Edge Computing requires a robust security plan & Cloud computing requires less of a robust security plan.
Some edge computing examples
- Voice Assistants
- Self-driving cars
- Healthcare
- Manufacturing
- Retail and eCommerce like geolocation Beacons
Challenges of edge computing
Challenges come with the implementation of edge computing applications are:
- Network bandwidth: With the implementation of edge computing, these dynamics shift drastically as edge data processing requires significant bandwidth for proper workflow. The challenge is to maintain the balance between the two while maintaining high performance
- Security: Edge computing requires enforcing these protocols for remote servers,
- while security footprint and traffic patterns are harder to analyze.
- The edge computing framework requires a different approach to data storage and access management.
Conclusion
With edge computing, things have become even more efficient. As a result, the quality of business operations has become higher.
To implement this type of hybrid solution, identifying those needs and comparing them against costs should be the first step in assessing what would work best for you.
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