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Often, cloud and edge computing are debated as mutually exclusive approaches to network infrastructure. They may function in different ways, utilizing one does not impede the use of others. Quite effectively, they actually complement one another in practice.

What is Cloud Computing?

In a cloud computing architecture, all data is gathered and processed in a centralized location. Generally, this data lies in a data center. To use the application that is associated with the cloud, all devices must connect to the cloud if they need it. Devices can’t get their data without access to the cloud if they are in need of it. Because everything is centralized, cloud is easy to control and secure while still allowing for dependable remote access.

Who are Cloud Providers?

Amazon, Google, and Microsoft are providing public cloud services such as Amazon Web Services, Google Cloud, and Microsoft Azure. These companies are also providing study material and taking exams for their certifications. For example, Microsoft is giving certification to them who pass their cloud exam of Microsoft Azure Fundamentals AZ-900. With proper AZ-900 preparation, an individual can get this certification and brighten his/her future.

The security level of the Cloud:

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Only authorized users can access information and tools to form the cloud. This makes the cloud more reliable and secure. A company can store information and possessions in a centralized cloud and users can access it anytime from anywhere.

Due to the centralized nature of cloud computing, data gathered from the edge of the network is difficult to process quickly and effectively. Cloud is deficient in speed, but it makes up in power and capacity. Cloud computing is based upon a scalable data center infrastructure that’s why Cloud can expand its processing capacity and storage as required. This scalability is a huge benefit for small businesses that are looking to expand quickly.

Constraints of Edge Devices:

because edge devices only accumulate locally collected data, any kind of big data for analytics can’t be processed by edge devices. This makes it problematic for edge devices. Things that are impossible at the edge of the network like analysis of big data, cloud computing is capable of doing them.

Huge amounts of data can be gathered by cloud with its processing potential and unparalleled storage. The cloud can gather massive amounts of data to produce valuable solutions, trends, and insights in a variety of ways. With the data analysis capabilities of cloud computing, Machine Learning and artificial intelligence are now more viable.

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Cloud computing is a valuable source for Datacenter infrastructures. IoT devices represent an exciting new frontier in the tech industry, by moving assets to the edge of the network. Not all businesses will see much benefit. For instance, by hosting their products in a centralized cloud, many service providers for the cloud can provide better security and other services.

What does Edge Computing mean?

A huge amount of data is being generated on the outer “edge” of the computing networks as the internet of things devices become more common and incorporate more processing power. The data is produced by IoT devices. Generally, the data created by IoT devices is conveyed back to a central network server. These servers are stored in a data center. Once the data is processed, further instructions are sent back to the devices out on the edge of the network.

But this setup has two problems:

1- Travelling Time

2- Strain on Bandwidth

Traveling Time:

The first is time to travel. For processing, it takes time to travel from the edge of the network to the center. Sometimes, it is just a postponement of milliseconds. Sometimes it can be grave. Even if the postponement is of milliseconds it is still meant gigantic while processing.

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The Strain on Bandwidth:

Secondly, all the data that is traveling back and forth puts tremendous strain on bandwidth between the center of the network and edge. The combination of high volume and distance can slow down the network to creep or network can even get worn-out.

Network latency can have serious sequences for IoT devices. For instance, consider self-driving cars. Autonomous vehicles gather a tremendous amount of data from other devices and their surroundings. Now think for the smallest interruption of response. It could literally be a matter of life and death.

Ending words:

Choosing edge or cloud computing isn’t an “either/or” proposition, fortunately. As the Internet of Things (IoT) devices become more powerful and widespread, organizations will need to implement operative edge computing architectures. Organizations can leverage the potential of this technology by implementing these architectures properly.

While lessening their limitations, companies can exploit the potential of both approaches by incorporating centralized cloud computing (fog computing) with edge computing. This can be done by collocating IT infrastructure with a data center.