Nowadays, we hear a lot about the great development of cloud computing on advanced computers, but there is some confusion around this concept. Many people believe it will eventually replace traditional cloud structures, though, this is not true. However, there are cases where the advanced architecture offers advantages over fully centralized cloud design, especially in terms of network and data storage. Although some people believe that edge computing can completely replace cloud computing, these two methods complement the replacements. It can also be used efficiently for more time-efficient data processing.
In general, the cloud is easy to protect and manage because everything is centralized, but it still provides reliable remote access. One of the main disadvantages of data processing is that they only store data collected on-site. And that prevents them from using any “big data” analysis. Cloud computing allows for large-scale computing at the edge of the network, which is currently not possible. Cloud computing can collect large amounts of data due to its amazing storage and processing capabilities.
Edge computing is a different strategy than cloud computing, unlike the Internet of Things. The only thing is to store real-time data near a data source that is the edge of the Internet. Regardless of the cloud, data center, everything is related to running applications that generate data. There are two problems with this system. First, it takes time to transfer data from the edge unit to the processing unit. This delay can be as short as just a few milliseconds, but it can be significant. This combination of high traffic and long-distance traffic can delay network indexing. Network delays can have serious consequences for I-o-T applications. Take self-driving cars for example. The response time of the vehicle thus depends on the commands of the computer resources in the network center.
Cloud Computing vs. Edge Computing – Let’s Explore!
While it’s not necessary to collect all the data in the cloud’s central memory, it doesn’t make sense to lose exclusive bandwidth when moving. This design offers the opportunity to reduce the cost of the I-o-T network by using, for example, technologies such as mobile technologies that use cheaper kilobytes to charge instead of a more expensive fixed connection.
With edge computing, you have to keep in mind that data, since it is not stored in the long run, is eventually deleted, which does not help in big data analysis. Note that edge computing only produces results through on-site data processing. In most cases, the collected data is easily deleted. So, if your I-o-T project requires the storage of data collected for all collected analytical purposes, then edge computing is not the right solution.
This means that edge computing is very suitable for I-o-T implementation, where local and batch processing can be used. An example of this is the distance sales invoice and retail inventory, and then sending the calculated results to the company headquarters according to the daily schedule. Probably not all companies need real-time data on every transaction. Instead, you can process this data on the spot and create a simple close-up report.
This methodology also reduces the storage space that many companies face when using traditional cloud computing architectures. Real-time cloud data streaming can be quickly merged. This information is often unnecessary. Yet, companies often worry about deleting data from the cloud and can lose thousands of dollars by storing data they probably never use.
As we move to the digital enterprise concept, edge computing models will become a key element of many I-o-T companies. When done correctly, edge computing allows parts to deliver I-o-T applications at a much lower cost than traditional cloud computing.
However, edge computing refers to the processing power at the edge of the network, rather than storing its processing power in the cloud or central data storage. Edge computer pushes the boundaries of IT applications, data, and services from centralized nodes to logical networks. All the same, allows the analysis and creation of data in a database. It can also be A-T-M’s (the bank wants to stop fake financial transactions); retail trade that uses signals to enter incentives to trade in mobile applications; smartphone; a transmission device that collects data from other endpoints, etc. before sending to the cloud. However, you can create an analysis model or rules in the cloud and then transfer them to a device.
Cloud Computing Is Not Enough–But Why?
Given the amount of data currently being processed, cloud computing is not the best choice for intolerance and disturbing computing programs. Here is the prediction of the growth rate of this industry in 2022: IDC claims that by 2022, the total number of installed I-o-T devices will be 28.1 billion. Instead, they send all that information to the cloud, leading to data centers and network blocking. Performing calculations closer to the data source will help reduce the overall dependence of your services or applications on the cloud and speed up data processing. Therefore, only data processing results should be sent over networks. In some situations, it gives accurate results and uses much less network bandwidth.
Which One Should We Choose?
However, the choice for lighting edge or cloud computing is “none”. Internet devices are bigger and more efficient. Companies need to adopt effective advanced computer technology to maximize the potential of this technology, and also need to gain AWS certification for their workforces for better analyzing the circumstances. Businesses can maximize the capabilities of both methods, but minimize their shortcomings by combining front-end and integrated cloud computing. Many try to do this by placing their computer technology in a data center.
Integrate advanced computing capabilities into cloud computing and computing power. Businesses can quickly launch their Internet devices, either high-tech or cloud computing, but somewhere in the middle. How companies are trying to grow and become more competitive by applying these two models. They will surely find new ways to maximize the desired benefits and address their shortcomings.