Edge Computing
Introduction
In present age of innovation-driven world, we have IoT devices, 5G fast wireless, mobile applications and smart dashboards, all are generating enormous data and sent out up into the Cloud for processing these mass information into helpful information.
Issues associated
But there are some issues & problems with this setup, as these sensors don’t transmit data regularly and security concerns are associated as well when data acquisition and processing are separated by hundreds of miles.
Likewise, what happens if the Internet link of facility gets interrupted somehow? Cloud would certainly be worthless then.
Here comes EC (Edge Computing) to rescue you. Let’s discuss this
What is Edge Computing?
Simple version, it is a computer that resides close to your sensors, collects data, and can do data processing. This way, instead of transporting all that data to the Cloud or to your data center, you deal with it locally first.
The “computing” part can be as tiny as a Raspberry Pi to a rack full of servers with multiple GPUs running the latest neural networks.
Image Source : IEEE Website
Working mechanism
By this, you can do refining as close to data acquisition as possible. This not only saves cost of data transfer expenses and also saves storage cost, if you can clean up the data before uploading it, since you do not require 100% of the video clip.
Edge Computing allows you to process sensor data before involving a data center.
Image Source : Wiki Website
Another benefit to think about: if your network goes down, the edge device(s) can store the data until network connectivity is restored and upload it at that point.
One way to solve this is to use an edge device. The sensors submit their data to this device and the device manages uploading the data to your data center. If there is no Internet connectivity, you can generate a report from the readings stored on the edge device.
Edge Hardware
The selection of hardware is dependent on task you are doing either store-and-forward of the data or do you need complex computing needs.
The difference can be a $30 Raspberry Pi or a rack full of GPUs that’s > $10,000 a piece.
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Edge systems are just another production system
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Application of Edge Computing
Edge application services minimize the volumes of data that have to be relocated, as well as the range that data have to take a trip. That gives lower latency as well as lowers transmission expenses. Computation offloading for real-time applications, such as facial recognition algorithms,
Image Source : Network World
Cloud gaming is another notable use-case, where some aspects of a game could run in the cloud, while the rendered video is transferred to lightweight clients running on devices such as mobile phones, VR glasses, etc. This type of streaming is also known as pixel streaming.
Other famous applications include connected cars, autonomous cars, smart cities, Industry 4.0 (smart industry), and home automation systems.
Advantages of Edge computing
Edge computing has several advantages, such as:
Increasing data security and privacy
Better, more responsive and robust application performance
Reducing operational costs
Improving business efficiency and reliability
Unlimited scalability
Conserving network and computing resources
Reducing latency
Challenges in deploying Edge computing
Some major challenges people face while applying Edge computing include:
Remote connectivity and debugging, where multiple devices need to be identified and connected.
Model, firmware and data upgradation, as video analytics require machine learning model updates and some gateways need firmware upgrades.
Lack of trained personnel to manage the devices due to complexity of systems and technical barriers.
Ending Lines
Edge computing is essential and widely use specially with the edvent of 5G and raise of IoT . These include (but are not limited to) connectivity, bandwidth management, sparse/bad data, heavy computing needs, etc.
