Edge Computing Is Changing The Network



Edge computing is a way to streamline the flow of traffic from IoT devices and provide real-time local data analysis

The data produced by the Internet of Things (IoT) devices is now processed closer to where it is created than sending it through longer routes to data centers or to the cloud.

By doing this the computing closer to the edge of the network lets your business analyse important data in real time, which is a need for organisations across many industries like Healthcare, manufacturing, telecommunication, finance, logistics and many more.

There have been many presumptions that everything will be in the cloud with a strong and stable pipe between the cloud and edge computing which is not realistic.


What is edge computing?

Edge computing is a “mesh network of micro data centers that process or store confidential data locally and push all received data to a central data center or cloud storage repository, in an area of less than 100 square feet.”


It is referred to in IoT use cases, where edge devices would collect a huge amount of data and send it all to a data center or cloud for processing. Edge computing sorts the data locally so some of it is processed locally, reducing the backhaul traffic to the central repository.

This is usually done by the IoT devices transferring the data to a local device that includes compute, storage and network connectivity in a small form factor. Data is processed at the edge and all or a portion of it is sent to the central processing or storage repository in a corporate data center, co-location facility or IaaS cloud.


Why does edge computing matter?

Edge computing deployments are ideal in a variety of circumstances. One is when IoT devices have poor connectivity and it’s not efficient for IoT devices to be constantly connected to a central cloud.

Other use cases have to do with latency-sensitive processing of information. Edge computing reduces latency because data does not have to traverse over a network to a data center or cloud for processing. This is ideal for situations where latencies of milliseconds can be untenable, such as in financial services or manufacturing.

Here’s an example of an edge computing deployment: An oil rig in the ocean that has thousands of sensors producing large amounts of data, most of which could be inconsequential; perhaps it is data that confirms systems are working properly.

That data doesn’t necessarily need to be sent over a network as soon as it’s produced, so instead the local edge computing system compiles the data and sends daily reports to a central data center or cloud for long-term storage. By only sending important data over the network, the edge computing system reduces the data traversing the network.

Another use case for edge computing has been the build-out of next-generation 5G cellular networks by telecommunication companies. IDC, who studied edge computing, predicts that as telecom providers build 5G into their wireless networks they will increasingly add micro-data centers that are either integrated into or located adjacent to 5G towers. Business customers would be able to own or rent space in these micro-data centers to do edge computing and then have direct access to a gateway into the telecom provider’s broader network, which could connect to a public IaaS cloud provider.


Edge vs. Fog computing

As the edge computing market takes shape, there’s an important term related to edge that is catching on: fog computing.


Fog refers to the network connections between edge devices and the cloud. Edge, on the other hand, refers more specifically to the computational processes being done close to the edge devices. So, fog includes edge computing, but fog would also incorporate the network needed to get processed data to its final destination.

Many organisations & educational institutions are developing reference architectures for fog and edge computing deployments.

Some have predicted that edge computing could displace the cloud. Edge and fog computing are useful when real-time analysis of field data is required.


Edge computing vs. cloud computing

For industrial companies to fully realize the value of the massive amounts of data being generated by machines, edge computing and cloud computing must work together.

When you consider these two technologies, think about the way you use your two hands. You will use one or both depending on action required. Apply that to an Industrial IoT example, where one hand is edge and the other hand is cloud, and you can quickly see how in certain workloads your “edge hand” will play a more prominent role while in other situations your “cloud hand” will take a lead position. And there will be times when both hands are needed in equal measure.

Edge Computing vs Cloud Computing

Scenarios in which edge will dominate include a need for low latency (speed is of the essence) or where there are bandwidth constraints (locations such as a mine or an offshore oil platform that make it neither practical nor affordable, and in some cases impossible, to send all data from machines to the cloud). It will also be important when Internet or cellular connections are spotty. Cloud computing will take a more dominant position when actions require significant computing power, managing data volumes from across plants, asset health monitoring and machine learning, and so on.

The bottom line is this: cloud and edge computing are both necessary to industrial operations to gain the most value from today’s sophisticated, varied, and volume of data applied across cloud and edge, wherever it makes the most sense to achieve the desired outcomes.


Edge computing security


There are two sides of the edge computing security.

  • Some argue that security is theoretically better in an edge computing environment because data is not traveling over a network, and it’s staying closer to where it was created. The less data in a corporate data center or cloud environment, the less data there is to be vulnerable if one of those environments is comprised.

The other side of that is, some believe edge computing is inherently less secure because the edge devices themselves can be more vulnerable. In designing any edge or fog computing deployment, therefore, security must be a paramount. Data encryption, access control and use of virtual private network tunnelling are important elements in protecting edge computing systems.


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