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Edge Computing

Edge computing is a scattered computing archetype which carries computation and data storage convenient to the place where it is required, to enhance response time and also save bandwidth. The element of edge computing lies in media and content transmission networks that were built in the 90s to deliver video and web content from edge computation servers which are expanded close to users. In the beginning of 2000s, these types of networks derive to application components and host applications at the edge servers, coming from the first edge computing services for commercial purposes that hosted applications like dealer shopping carts, ad insertion engines, real-time data aggregators, and locators. In the present-day edge computing automatically boost its approach through virtual technology that makes it accessible to set-up and run in a ample range of operations on the edge servers.

Edge computing is considered as a growing market, It is likely to boom in 2020. With a predicted growth rate of ~50%, this industry is expected to have more users from software platform providers, telecom companies, public cloud providers, data centers, content delivery networks, and many more.

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Why Edge Computing?

By dealing with the proximity complication, you can deal with the discontinuation problem. The on-device approach to achieve results ensures that only non-complex data is delivered over the network and that complex data can be responds quickly. That is critical for sensitive operations, like autonomous vehicles having to wait milliseconds may be illogical.

The distributed approach of edge computing also reduces bandwidth. Data transformation begins at the point of collection and the data which needs to be stored is transferred to the cloud. This is because it makes edge computing more adequate, scalable and reduces network load.

For example, if we have numerous security cameras sending high quality video to the cloud, it will become a bandwidth problem. It’s impossible and excessive cost. Interruption, intermittent connectivity, and Outage reduction  are also improved with edge computing because it doesn’t exclusively rely on the cloud for processing. This can help in dealing with server downtime, assuring decent service application in remote locations and avert random downtime.

Basically, there is a supplementary  layer of security with edge computing, however most of the data comes from IoT devices that don’t cut across the network. Rather,  at its point of creation. Less amount of data in the cloud means there is less data to be in a breach.

However,  there are things to worry about edge devices themselves being accessible and vulnerable. The history of less-secure IoT devices, and nothing of hidden privacy concerns. Such devices are more expensive than wiretaps.VPN and Encryption channels will be highly important as edge computing thrives. 

Edge Computing Examples     

Edge computing has come up with the conception of IoT devices and has been built-in different circumstances. The network edge relies upon the use case. It can be a cell phone, telecom tower, self-driving car, an IoT gadget etc. In future There will be edge servers, micro data centers and edge gateways to help further local processing and cut down round slide data times to the cloud.

Automobile industries may be the best example of why edge computing is important. Such as Self-driving cars based on Artificial intelligence are loaded with many numbers of sensors gathering data to processes such as collision detection. These kinds of  vehicles can’t wait a second for cloud processing. It has to be enabled for data processing and that data right away and make predictions to convert into a decision.

Industry giants are estimated that self-driving cars will produce ~50TB of data a day by 2021. Companies like Toyota conclude that the automobile-to-cloud data transfer rate will reach 10 exa-bytes every month by 2025. All that data sent to the cloud is quite costly and irrelevant, not to mention untenable by present networks.


Edge computing is becoming popular these days with more grip during the last few years. Cloud services such as AWS and Azure had edge services in the IoT domain viz Azure Stack Edge and FreeRTOS. The most important thing about AWS and Azure Network Edge Compute are known for its adaptability of choosing the framework at edge or on any cloud for deploying models basically if we talk about machine learning models. The workloads depend on runtime necessity such as  payload volume, data residency, latency for running the use case. This computation system is capable of deploying conventional solutions like Virtual Machines and containers on the edge. 


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Badal Kumar

Data Analyst at Aeon Learning

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