Cloud and Edge

image source: Trantor

In Cloud Computing, most of the data processing through the existing IoT systems is performed within the cloud, using a series of centralized servers. Edge moves the processing away from the centralised servers, and closer to the end users. Due to huge amount of data generation, cloud’s data centers and networks are overloaded. The resultant increased amount of latency and inefficiency can prove to be an unsurmountable challenge for cloud-based data. Edge computing helps analyse data in a manner that is closer to the source of said data. Edge computing offers a flexible, decentralised architecture, which means that everything is processed on the devices itself instead of processing everything in the cloud, where you may find a data overload.

The internet architecture is strained due to heavy network pressure. Edge computing involves pushing data-handling to the edge of the network, closer to the source of the data. In other words, instead of sending data to the cloud server or central data centre for processing, the device connects through a local gateway device. This allows faster analytics and reduces network pressure.

The immense growth in mobile, automotive, and IoT creates a need for more processing power at the Edge. In a traditional cloud computing architecture, the actual processing of data occurs far away from the source. Edge computing pushes the generation, collection, and analysis of data out to the point of origin, rather than to a data center or cloud. The public cloud does the heavy lifting while the edge delivers the required intelligence. It is better to selectively incorporate both models: edge computing where time is of the essence, and cloud computing where security and volumes abide.

Edge and cloud are not inherently competing platforms or a binary choice. Rather, they are working in unison to allow smarter and faster devices to make real-time decisions, while also sharing data and connecting to one another.