Essentially, Edge Computing involves placing computing power close to devices and sensors that make up the Internet of Things, which are largely away from a data center.
By conducting the analysis locally, large data sets can be analyzed without incurring latency overhead that would otherwise occur in a cloud environment. A local connection ensures a high degree of resilience and better response to critical situations, allowing large amounts of data to be analyzed and scrubbed locally before being sent to the cloud for further analysis.
What are the benefits and features of edge computing?
Responding in Real Time
Streamline data without worrying about latency, enabling simultaneous computing with mesh architectures
Secure Data & Privacy
Data at risk in a given location can be reduced by processing sensitive information locally on edge devices.
Incorporated intelligence
Enhance the local processing power of devices at the source of data in order to run smarter and more sophisticated features.
Continuity
In case of loss or temporary unavailability of internet connectivity, ensure that edge devices continue to function and communicate with each other.
Scalable
As requirements evolve or operations grow, rapidly iterate, expand, and evolve edge applications.
Efficient use of data
Filter unnecessary data before sending it back to the main system and decentralize data processing to run as close as possible to the source
What makes edge computing unique?
A computer was originally a large, bulky machine that could only be accessed via terminals, which were simply extensions of the machine. Computing became much more distributed after the invention of personal computers. The personal computing model dominated computing for a period of time. Applications ran locally, and data was stored on-premises servers.
This locally based, on-premise computing has been largely replaced by cloud computing, a more recent development. An Internet-based cloud service is a set of data centers managed by the vendor and can be accessed through any device.
Because cloud services are hosted at data centers located far from users, cloud computing can introduce latency. While still keeping the centralized nature of cloud computing, edge computing brings computing closer to end users to reduce the amount of distance that data has to travel.
As a final note:
Early computing: Only one computer running centralized applications
Personal computing: Decentralized applications running locally
Cloud computing: Applications run on centralized servers, a central data center.
Edge computing: Running applications on the edge of a network or on the device itself, close to users.
Do edge computing applications have any other use cases?
It is possible to integrate edge computing into various applications, products, and services. Some examples are as follows:
IoT devices: To ensure better user interactions, smart devices connected to the Internet can run code locally rather than in the cloud.
Self-driving cars: A vehicle with autonomous capabilities must react instantly without waiting for a server to send instructions.
Caching efficiency: Content can be tailored to be cached more efficiently by applications running on CDN edge networks.
Medical monitoring devices: Depending on the device, real-time communication is crucial, without having to wait for cloud servers to respond.
Video conferencing: The backend processes for interactive live video consume quite a bit of bandwidth, so moving them closer to the video source can reduce latencies and lag.