What is Edge Computing and what are its applications

Edge Computing

Down the lane of technology and computing, especially from the Charles Babbage’s vision of digital programmable computing, we have taken a stroll too far. But boy are we stranded! We have modified computing in ways unimaginable as some of the most promising technologies of future have computing at the core. And because the digital world is focusing mainly to expand the horizons of wireless connectivity, remote and wireless computational technologies are one of the top priorities of software as well as mobile app development companies. We have seen Infrared, Bluetooth, and WiFi being invented, over the time, which have led us to advanced wireless computing technologies such as Edge Computing.

Edge computing is a type of cloud computing which uses wireless technology just like cloud computing, and it would even be safe to call it a branch of cloud computing. However, it is a step up from conventional cloud computing as it processes data on a network node itself, also called ‘Edge’, or a collection point near it. This reduces the hassles of transporting huge data volumes between data nodes and data centres, and relieving load on central data centre servers; hence the name Edge Computing. Due to low volumes of processed data to be transferred, the bandwidth required between data centres and sensors (nodes) is significantly reduced.

Some of the advantages of Edge Computing are:

Reduced Costs

Quite clearly, edge computing reduces data transmission and management costs over a network. This allows organizations to save quite much on batch processing of big data and hefty data streams, over a longer period of time.

Speed

Compared to conventional cloud computation, edge computing is faster simply because it doesn’t send over the data to central servers, rather processes the data at the source. This also reduces network latency as the data processing doesn’t have to wait for instructions from data centres for transmission and receiving.

Virtualization

Virtualization is not a new term, however, the way edge computing changes virtualization is remarkable. Not only has it created opportunities for virtual computing due to its faster computation model, it is also an improvement in terms of scalability as more and more sensors can be connected to the network and generate data, without actually affecting the load on server. However, that would require a wider bandwidth to handle the increased number of nodes.

Security

Enterprises have had long standing trust issues with enterprise data and confidential information travelling across the World Wide Web, where a skilled hacker can sabotage security lines with just a few lines of code. So maybe the solution is not in making the network secure, but in managing the movement of data securely.

Applications of Edge Computing

Sensors

Sensors are everywhere around us. They are in the elevators, automatic doors, security checks etc., and collect data as they are used. With Internet of Things connecting various components that build our network, edge computing plays a big role in helping IoT app development companies to bring IoT solutions to reality for many enterprises. Although IoT is a great idea to connect different elements and layers of technology around us for effective sharing of data and harnessing it for improvements, for data centres, it means more raw data incoming, more processing loads, and maintaining an equivalent throughput. This is where edge computing steps in and reduces the volume of data produced by IoT nodes or simply IoT sensors, thereby leaving a less complex version of data streams for servers to handle.

Augmented Reality

The salient features of augmented reality, and also the one it is considered most unique for, is that it allows you to experience in real-time, the virtual vision it creates. It scans our surrounding, sends the data to servers to process in real time, and respond at the user’s end. Currently, one of the biggest challenges Augmented Reality technology is facing is need for higher bandwidth for AR solutions to reduce latency in responses. Edge computing considerably reduces latency in the network by processing the data at the source itself i.e smartphone, AR gears, etc. Many industries that deal in Augmented Reality have edge computing architecture to reduce load of real-time processing requirements of AR, with a focus on reducing end-to-end latency. But then it is not just the edge computing that creates a tangible difference but also the strength of communication channels.

Edge Data Centres

While end-to-end latency is what AR solution providers aim for to give users a smoother and glitch-free experience, most of the data centres make sure that they don’t burn out in terms of productivity and throughput. Hence, they go for a small, local data center in different locations to make the data travel lesser distance. So in a way, it is not exactly edge computing, and rather means moving the data centres, close to the source of data generation. This means data can quickly transfer between the data centres and generation nodes, which is close enough to being called ‘processed at source’.

Edge computing has a big advantage over cloud computing for the simple reason that it is faster, more scalable, more secure and has a wider range of applications in consumer level services like mobile app development, web services, and even home automation. In the upcoming years, edge computing is set to participate in most of the computing requirements in future that are set to branch from smart technologies like AI, Machine Learning, IoT, which partly depend on data generated by sensors.

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