There are currently more than 3.5 billion smartphones. These handheld computers allow us to do a lot of things and are our gateway to a giant data network, the “brain” of modern computing.
This centralized model has worked well so far, but with the advent of IoT (Internet of Things), we are starting to realize that we are using a system that needs to evolve.
Index – The Impact of Edge Computing
What is Edge Computing?
It is a perimeter data analysis system located halfway between the cloud and the device itself, from which a command or signal is sent to be analyzed.
most local treatment a device can do, the less it will have to rely on the cloud and therefore the faster it will be. This IoT problem is one that advanced computing aims to solve.
The following video explains how it works 👇🏻
The difference between Edge Computing and Cloud Computing
Voice assistants, such as Siri or Alexa, usually need to resolve their requests in the cloud and this round trip time can be very long.
Latency, even if it is only a few milliseconds, can be crucial, for example, in the case of autonomous cars where immediate responses are needed and latency can mean the difference between life and death.
In edge computing, instead of delivering data to a central server, the device itself collects and processes data in real time, responding faster and more efficiently.
In cloud computing, all data is collected and processed in a centralized location, typically in a data center. All devices that need to access this data or use associated applications, must connect to the cloud first.
But what he lacks in speed, the cloud compensates with power and capacity. By leveraging a scalable data center infrastructure, you can expand your storage and processing capacity as needed. This complements peripheral devices which can only accumulate data collected locally.
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Impact of Edge Computing on IoT
The concept of IoT refers to a digital interconnection of everyday objects with the Internet that allows us to interact with them.
The IoT is growing at a very rapid rate and with it, the amount of data it produces. Handle this huge amount of information is a challenge and advanced computing could be part of the solution.
# 1 improve data security
With the Internet of Things, the number of connected devices increases, as does exposure to security attacks.
With edge computing, each device would have the ability to process and store its own information. This data, which is not transmitted to central servers, reduces the likelihood of cyber attacks.
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# 2 Speed
The bandwidth savings afforded by edge computing would be another major benefit for IoT devices. They could store only important information for their purposes and throw the rest away, avoiding sending unnecessary data that slows down the Internet.
In addition, if the information is stored and processed in a local database, sending data to the cloud is no longer necessary for each interaction, which reduces response time.
# 3 cost
The bandwidth savings will not only impact server speed and footprint, but also the logistical investment in maintaining and increasing computing resources to keep information flowing. Cloud costs will be drastically reduced.
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# 4 Resource use / scalability
Edge computing makes it possible to create extensions to IoT devices in combination with local centers, according to their needs. This scalability is less expensive and can be applied flexibly.
# 5 Improved application performance (mobile edge computing)
Improved speed and latency enable responses that occur in real time and automatically respond to stimuli detected by sensors in IoT devices.
Will this be the end of Cloud Computing?
For example, when we do a Google search, this little stream of information is sent to the cloud, is processed, and then the information we need comes back to our screen.
IoT devices collect a lot of information which must be processed in real time. There is no time for this data to come and go in the central cloud, so some people think the cloud computing era is coming to an end. However, we do not believe this to be true, as cloud computing complements edge computing In many ways.
Even though most of the immediate processing and decision making is done on the device itself, there will still be a demand for a centralized cloud to store the most important information.
At the edge, detection, inference and action will occur, while the cloud will focus on basic learning. Then this learning, with this up-to-date information, will move to update each device to keep it operational and always up to date, without us having to do anything.
One of the fundamental aspects of machine learning is this it needs a lot of data to learn. A model that combines the edge and the cloud has large amounts of data entering a centralized point which makes devices smarter and smarter.
The future of network infrastructure is unlikely to lie only at the edge or in the cloud, if not somewhere in between. As companies seek to transform and become more efficient, they will most likely have to integrate these two models to find new ways to make the most of their respective strengths and use them to overcome their weaknesses.