What is Edge Computing?
Edge computing refers to the computing that is done near the edge of the data or the cloud. This type of computing helps minimize the latency and bandwidth used.
Technology has evolved from big bulky computers to smartphones, and IoT has revolutionized the world. Almost everything that had the potential to get centralized is centralized using cloud computing. Today, cloud computing act as the backbone of many big companies. And getting the data processed fast is a luxury sometimes, but in some cases, this becomes a necessity. Like in healthcare, self-driving cars, and financial services delay of seconds can have a lot of impact on the outcome.
Sending data on the cloud for computing can cause little delays and also require a lot of expensive infrastructures. With the boom in IoT, more and more devices are connected to the internet and are generating data. It is estimated that by the year 2025, 460 exabytes of data will be generated daily throughout the world. Cloud computing may not be able to handle this bulk of data, and this is where the this computing comes into play.
it`s brings the computation and data storage closer to the edge. Edge is the place where data is being gathered or created. It processes the data locally rather than relying on a central server that is situated thousands of miles away from the devices. This helps the systems to eliminate the latency, and it also saves a lot of money by reducing the cloud infrastructure.
The benefit of Edge Computing:
Let us have a look at the benefits that edge computing provides.
Many applications around you require cloud base computing in order to function. Such is the example of a voice assistant; the assistant requires the cloud to translate the user voice into executable instructions. For that, it needs to send data to the cloud, the process of sending the data to the cloud, and receiving it back causes some delay. This kind of delay is referred to as latency.
In cloud computing, these clouds are present thousands of miles away, which causes this delay. If the same task is done by edge computing, the latency drops drastically. This is because you don’t depend on the cloud anymore for computing. All the processes are carried out on the local edge server rather than a centralized one.
There are many critical situations where all of the data is sent to the cloud for processing, and the system relies on the output to take action. These systems become highly vulnerable to intruders, and even a small disruption can cause the entire system to crash. When this data is distributed among the local edge computing network, the risk of any disturbance from the hackers reduces.
Edge computing may increase the potential attack surface for hackers, but the impact of those attacks, on the whole system is reduced by a lot. This system of local edge computing helps big companies to overcome the issue of privacy and security.
With the increase in IoT devices across the globe, the size of data sent and received to the cloud also increases. For example, if you have a signal device that sends live footage to the cloud, you can stream all of it to the cloud without any problem. But if the number of devices increases, the data size also increases. A considerable bandwidth is required to send and receive this much data to the cloud.
Introducing the edge computing allows the user to discard the unnecessary data and save the required footage to the cloud. Doing this pre-processing on the edge nodes will enable the user to reduce the size of data that is to be sent. Hence, reducing the bandwidth.
Edge computing gives the computing power to the devices and allows them to act in real-time with low or no connectivity. The technology is in the initial phases, but there is a lot of potential to it, and it can be seen everywhere, from self-driving cars to agriculture. Here are the few industries that use edge computing
In the current era, the use of health monitoring wearables is considered very trustworthy and safe. These wearables devices include smartwatches, fitness trackers, and glucose monitor system, etc. Most of these devices are connected to the cloud, and others operate offline. In order to utilize these devices to the fullest, we need real-time analysis. This is made possible by analyzing all the data on the edge node.
The edge computing is also used for remote patient monitoring and in healthcare management. Hospitals and clinics can provide better service to the patients alongside good security for the patient’s data. These edge nodes also use big data and AI to predict patterns for certain diseases like cancer.
Farms are mostly located in remote areas that have connectivity issues. This makes the edge computing the best option for agriculture and smart farms. Edge computing can be used to analyze the data collected through the sensors locally. The local computation will work even if the cloud is inaccessible hence eliminating the connectivity and bandwidth problem.
Nowadays, most of the banking services have shifted to the digital method along with the traditional ones. These banks are using cloud computing on a large scale to store and process the data of their users. For this amount of data, these companies require a considerable amount of bandwidth to use cloud services.
In order to reduce bandwidth usage, the banks have shifted to edge computing. By shifting to edge computing, banks not only saved the bandwidth, but they also minimize security threats. By doing the computation locally, the services get a lot faster than usual and secure. Other than the banks, edge computing also allows the financial services providers to make quick decisions. These decisions require real-time processing based on market shifts, so edge computing is the best option for such tasks.
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