Edge Server Guide: Why Are They Suitable for Future Applications?
The computational power of the edge server is speedily shaping the modern industrial landscape. The emergence of dynamic applications and modern business infrastructure has made the fast processing and sharing of data inevitable. Companies are now replacing traditional centralized servers with edge server technology to meet this growing need.
What Is an Edge Server?
In a centralized network, client devices are connected to one server or multiple machines whose job is to process the information requested by the users and hand it back to them.
While a centralized network is good enough to interact with simpler websites, companies have found that complex projects involving a great bank of customers work better with an edge server.
Unlike a centralized network, an edge server sits at the edge of a network. This change of location solves multiple issues common with a centralized network. Edge servers improve latency, reduce loading times, and remove the load from the origin server.
Instead of sending unprocessed data to the data centers, edge servers process it themselves and send it back to the client machines.
Edge Servers Can Be of Two Types:
Edge Compute Servers – this server provides computing at a network’s edge and is usually used for IoT apps.
How Does an Edge Server Work?
An edge server usually performs more specific tasks such as network functionality, inference in AI-enabled workloads, etc., instead of focusing on large-scale generic tasks.
An edge server works by “interfacing” between two different networks. This means that it helps exchange information between two different networks without causing any kind of latency or unnecessary exposure between them.
For example, let’s say you requested some banking information through your online account. Instead of exposing their backend data and information storage to the internet, banks employ an edge server that not only offers up a website interface to the user but also culls the relevant information from the backend and transfers it to the client computer.
This way, an organization can expose a much smaller portion of its business backend to the internet. This ultimately narrows their point of entry, making them more secure. For specific use cases, such as this one and others (think surveillance cameras, industrial equipment, sensors, video streaming, etc.) edge servers offer data in a more granular form.
How Is the Relationship Between Edge Cloud and Edge Servers Booming?
The concept of edge cloud computing emerged from the need to bring applications and data closer to one another. Their job is to bring a lot of granular data to the machines or humans requesting it.
Traditional centralized data centers have struggled with latency regarding service for the numerous emerging applications. This was highly noticeable with video/content delivery networks, cloud gaming, and automotive apps, which became the most significant revenue drivers for the edge cloud.
For years now, the large centralized data centers (buildings that store and share applications and data centers) and cloud architectures provide data sharing and processing services to individual users and enterprises with Software-as-a-Service (SaaS). Even today, the cloud is how we access or consume content, whether it’s live video streaming or requesting bank details.
But now, there is a new generation of cloud-native apps on the rise in all industries, from entertainment and retail to automotive and manufacturing. These industries are starting to become more and more compute-intensive and sensitive to latency.
To ensure the quality of experience for the clients, enterprises can no longer rely on traditionally centralized cloud architectures. They need a more dynamic and distributed cloud model that can serve the growing demands of emerging networks.
In other words, cloud service providers are now shifting their compute and storage cloud resources closer to the edge of the network where the content is created and consumed. This is called an edge cloud. It is an interchangeable cloud ecosystem with incredible storage and computing powers, which is interconnected by a scalable and adaptable network.
Edge servers formulate edge cloud architecture in which these are smaller in size but highly specialized in performing latency-sensitive tasks. Central cloud servers are more consistent and less specialized.
In a real-world analogy, general workloads deployed in the cloud are like huge Lego boxes with which you can create large items utilizing enough time. On the other hand, edge servers are like party-sized Lego sets with only specific shapes available.
This edge cloud/edge server teamwork is highly beneficial for the working of the latest microservices-based applications. It allows for a granular approach with a deep focus on “what should run where” decisions.
What Is the Future of Edge Servers? Think 5G and AI Needs
Experts believe that the massive computing power of edge data centers has made it an attractive choice for training machine learning and AI-based models. AI and Machine learning models can easily overwhelm the on-premise environments. By employing an edge server, companies can greatly optimize the back and forth processing and sharing of information between the cloud and on-premise models.
According to a prediction by Gartner, around 75% of all enterprises will generate and process data outside of a centralized data center or cloud by 2025. By employing a local edge server with a cloud-trained model, companies will be able to easily leverage the cloud computing powers without having to struggle with data overloads, in particular for specialized uses that are more common in larger enterprises and industries.
And 5G plays a key role in today's many applications. There is so much more than 5G can offer and we have not even fully assessed the possible impact of these networks once their potential is combined with the edge servers’ computing capability. Not only have edge servers begun helping telecommunication companies deal more effectively with data in real-time, but are also the future of AI and machine learning, smart cars, cameras, and emerging IoT technologies.