Edge Computing: The Best of Two Worlds
L to computing edge (edge computing) is an expression of quite fashionable. If you ask any computer company if they do edge computing, the most likely answer is yes. The word remain so overused that, in many cases, it has almost lost its meaning, simply meaning, ” Yes, we have an industrial PC somewhere, with a program running .” But this, in reality, does not reflect the essence of edge computing and, without a doubt, does not reach its full innovative potential, mainly in industrial applications.
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The Age of Cloud Computing
The history of computing shows various waves of local versus centralized data processing. In recent years, there has remained a strong trend towards cloud computing: data management and computing are moving to large centralized areas of servers, while personal computers remained limited to hosting a web browser.
Today, companies make use of managed file transfer software to ensure accuracy in the transmission of data in bulk. It also boasts of speedy delivery to recipients to ensure that the business fulfils its promise to its clients.
This kind of software solution ensures a high level of security that complies with data security laws and regulations. The transmission of data is very accurate since it doesn’t have human intervention. It also reduces downtime and with one press of a button, you can always resume the transfer in a secure manner.
Managed file transfer software can be customized to suit your business needs, and its scalability and flexibility to enhancements and upgrades make it a cost-effective investment. Since data are encrypted, even if it gets to the hands of the wrong people, the file may not be useful to them as they would need a decryption tool that only the partner company possesses.
The benefits of managing data in the cloud are clear:
- Quick and easy software updates, as they can remain managed from a centralized source;
- A global and integrated vision of all the workflows and teams connected to the cloud, which allows making decisions both at a worldwide and local level;
- A central place with all the data for further optimizations.
Data Management Limitations
On the cloud
But this bright new future also has its downsides. With the overall transition to IoT and cloud-connected machinery and logistics, new challenges await us. Today, when real things, such as cars, buildings and turbines, come equipped with a digital twin, more data remain collected for processing in cloud data management applications.
For any unusual occurrence that could happen, the cloud can be a mine of information to aid anyone, in disaster recovery, check the archive for references and store solutions for future use.
For example, if vibrations in a machine get out of control, they can easily cause a chain reaction, capable of disabling the entire production line. Consequently, modern control systems come with an increasing number of sensors placed in the machine’s key parts, which are in charge of detecting these vibrations and sending their data to the operating system, in the cloud, for continuous analysis and specificity. Imagine all these little sensors producing data in an industrial production environment – the data load is enormous.
And that is where one of the main challenges of managing data in the cloud appears. If you are working with data processing in the cloud,
There are likely to be several drawbacks:
- Physical limitations of data transfer (still bits and bytes cannot travel faster than light!);
- Dependence on network availability (when the network goes down, control and optimization from the cloud also goes down);
- Data loading (even at high speed, a data transfer of this magnitude is too slow);
- Data privacy (there is data that industrial companies do not feel comfortable sharing in the cloud);
- Cybersecurity (the transfer of data within systems in the cloud always offers a vulnerability to theft. So the transfer of data in systems that operate in the cloud remain generally confined to uploading data. Since the download is still more sensitive to cyberattacks).
Handling a large data load is easier when the data remain processed locally. Are we ready for a revival? Is the wave of computing history reversing towards local data processing? Yes and no. And that’s where edge computing comes in.
Edge Computing
The computing edge is not new. The biggest players in computing, such as Cisco, have used it for years and its sister in fog (fog). The real innovation has to do with the latest integration with industrial production processes and their optimization within systems that operate in the cloud.
Edge applications, such as Analyze MyWorkpiece, offer the ability to collect and analyze data close to where it devises within the production process. For example, Siemens remain also integrated with MindSphere. An operating system from IoT in the cloud. In edge computing, data processing is not exclusive to the core of the cloud. Still, it fundamentally occurs at the periphery, the edge of the Internet, where it comes into contact with the physical world.
Integrating edge computing in industrial clouds enables us to take advantage of the benefits of cloud systems. Such as quick and easy software updates while taking advantage of local data processing, at the same time, such as data security, rapid application reactions and the control environment within an industrial production process.
This is especially important in industries that rely on modern technological machinery to save lives like the ones you see in hospitals. Thousands of data transmitted need to be analyzed by experts to make decisions to improve the lives of patients on the line.
Why is Industrial Edge Computing the Solution?
There is a lot of enthusiasm for industrial edge computing, even though a few concerns arise:
- The risks of implementing digitization;
- The acquisition of a niche solution with little scalability;
- Investing in a prototype that doesn’t work for all machines.
But with industrial edge applications, these concerns are easy to address. They offer fully integrated, deployable, and cost-effective solutions on critical issues such as maintaining privacy and dealing with heavy data loads, achieving the improvements promised by digitization while leveraging data management in the cloud.
