Edge computing is a distributed IT network architecture that intends to improve computational processing efficiency. The purpose of this is to bring the processing power of computer data into closer proximity to the devices accessing the data.
This closer proximity reduces the use of bandwidth and storage requirements of the network being accessed by devices such as PCs, smartphones, and the Internet of Things (IoT).
Why has Edge Computing Become Important?
It is the relatively recent proliferation of IoT devices such as sensors and smart tech that is driving edge computing technology development. Smart IoT applications in autonomous equipment, vehicles and other technologies either communicating with or controlled through the internet, are all constantly processing low-latency data. This means both the bandwidth being used to access the internet and the storage capacities of networks are being pushed to their limits.
The IoT data can now be processed closer to the logical edge of its related network instead of sending it to cloud data centers. This decentralization of processing power improves real-time processing speeds, reducing latency which is the key to improving bandwidth efficiency and storage capacity.
Why Reducing Latency is the Key
Latency is the delay between a computational action and the response, so edge computing aiming to reduce latency is the key to its potential.
For a very simple example, a voice controlled IoT device usually resolves requests in the cloud and there is always a noticeable delay between request and resolution which is because of latency. This latency is caused by the device needing to process the voice command, compress it into a form that can be sent to the cloud before sending it to the cloud and awaiting the response. The resolution is then sourced within the cloud, compressed, and sent back to the original IoT voice device, which then decompresses the data and processes it into a human-like voice to present the resolution. This happens fast; though we humans always want to go faster, and so here we are at edge computing.
We will use edge computing to store packets of relevant data closer to IoT devices which will then be able to send and receive certain necessary data even faster, reducing the latency period between request and resolution.
The Future of Edge Computing
As well as voice assistance technology, edge computing technology is already being implemented throughout many different industries, and it is only going to become far more widespread.
Autonomous driving technology is going to be heavily reliant on this technology as the vehicles must be capable of reacting to incidents in real-time. Improving on the 100 milliseconds or so it takes for data to be transmitted between a vehicle sensor and its backend cloud datacenter could be the difference between a collision and a near miss.
Other applications that will inevitably incorporate edge computing include fleet management and predictive maintenance.
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