Cloud computing established itself as the standard of enterprise computing a while ago. While this stays true for web-based applications, other industries are seeing a shift. Edge computing has emerged as a swiftly growing paradigm, with more and more embedded applications relying on edge architectures. 

But what’s the difference between Edge and Cloud computing? And why is Edge gaining ground? Is AI related to this new trend? Let’s find out.

Edge vs Cloud: Two Different Approaches

Edge and Cloud computing differ not just in terms of hardware, but in the fundamental approach.  

Cloud computing is about concentrating the computing power in centralized cloud servers, instead of the actual systems running on-site. The idea is to remotely access these servers for storing data and performing computations, with a large number of machines relying on a single cloud computer.  

Edge computing, on the other hand, advocates for having the computation happen on-site. Instead of transmitting the data to a remote cloud server, the data is processed immediately by the embedded computers themselves and also stored locally. This approach is more decentralized and does not rely on remote connectivity.

Why Was Cloud Computing So Popular?

Cloud computing was (and in many industries, is) the preferred architecture because of its cost efficiency and reliability. A cloud server can handle all high-intensity requests, saving the deployed embedded systems from the burden, which means they can have basic features and do their jobs.

This also reduces the resource usage of the deployed systems and ensures that any breakdown in these does not affect overall performance, as the data is stored and managed in the cloud server, insulated from these issues.

The Solution: Edge Computing

To solve the challenges experienced by relying on remote servers, a different way of computing was designed. As the name suggests, Edge computing puts the computing power at the edge itself, where the embedded systems are deployed. This eliminates the need to transmit the data, and cuts down on the latency responses.

The faster processing is crucial for applications like industrial automation and smart manufacturing, where the machines need to make split-second decisions based on the data captured. And for applications like healthcare or IoT, this ensures data privacy and security, since nothing is held on a third-party system. 

The Artificial Intelligence Factor 

Most of the factors we mentioned earlier have been true for years. But there is one new factor that has become relevant recently, and is behind the big push toward edge computing. And that factor is AI.

AI tools rely on vast amounts of data processing to give results. Sending that data to a cloud server takes time, making it impractical to use in most applications. In computer vision, for example, the machines need to quickly identify the objects and take action accordingly.

This is why AI in embedded scenarios relies on Edge computing. Powerful rugged computers with GPUs are capable of crunching the data on-site and producing actionable results, which is more efficient than sending a constant stream of highly dense data back to a cloud server. 

Is Edge Computing the Future?

The growing effectiveness of AI in embedded and IoT applications is pushing the industry toward computers that can handle this workflow. This means that Edge computers are taking over, armed with powerful hardware capable of AI processing. 

Edge computing is certainly the direction to invest in for any industry, since AI algorithms are changing things in all niches, from healthcare to education to smart manufacturing or even warehousing. That being said, cloud computing still has its uses, especially in applications where data processing isn’t involved, and instant computation is not needed. 

The good news is that Cloud computing doesn’t need any special hardware from the client systems – which means that a PC meant for Edge computing can rely on the cloud as well, where needed. This is what makes Edge computers the best investment for any embedded application.