Edge Computing Architecture
Edge computing differs from Cloud computing in that the data processing is performed at the edge of the network near the source of the data, instead of in a central data warehouse or in a cloud. This optimises cloud computing systems.
Edge analytics refers to data analysis done at the edge of the network. The actual analytic rules or model could be created in a cloud and then pushed out to edge devices, meaning that Edge computing does not replace cloud computing.
Because the analytics are performed near the source of the data this reduces the communications bandwidth needed between sensors and the datacentre. Edge analytics involves leveraging devices that may not be continuously connected to a network such as smartphones, laptops and sensors.
It is also important to note that some edge devices are not capable of doing analysis.
Other Tech and Terms
Edge Computing includes a wide range of technologies such as mobile data acquisition, mobile signature analysis, wireless sensor networks and more.
Edge computing is closely related to fog computing, which refers to peer-to-peer ad hoc networking and data processing from the edge to the cloud. Other terms used to describe this classification of computing are local cloud, grid computing, mesh computing and others.
Also data warehousing, which is massive storage of data where slower retrieval of data and analytic queries are required.
What are the advantages of Edge computing?
Lower transmission costs
Better QoS (Quality of Service)
Edge application services considerably decrease the amount of data that needs to be moved, resulting in less data traffic, and a reduction in the distance the data has to travel.
A major Bottleneck is removed
A potential point of failure is limited.
Edge computing moves function away from a central computing environment.
Encrypted data is stored at the network core. Incoming data is checked as it passes through protected firewalls and other security points meaning viruses, compromised data, and active hackers can be caught at an early stage.
The ability to virtualise, grouping CPU capabilities logically, on an as-needed, real-time basis extends scalability.