Edge Computing Architecture Explained: Layers, Components, and Real-World Design Models

It is difficult to define edge computing with a single explanation because it may be utilized in so many different ways. You may ask if it has to do with smart buildings, smart home appliances, or smartphones. Could it be a form of cloud computing or something that operates locally? Is it the speed of the internet, the way computers connect, or the way they think? Are self-driving cars, hospital screens, or factory robots involved? Edge computing is a component of each of these and more. In this blog post, we'll go over the key concepts of edge computing architecture, along with the setup requirements. However, it's important to keep in mind that edge computing is really about data, where it’s processed, how it’s handled, and how it’s shared.

What is edge computing architecture? 

Edge computing is a computing approach in which collected data is processed at the edge of a network rather than being sent to a centralized server for processing and storage. The goal of edge computing is to process data at its source, or as close as possible.

For example, a surveillance camera with edge computing capabilities can capture data and process it immediately on-site instead of transmitting it to a server hosted at headquarters for further processing and analysis.

Businesses are increasingly using edge computing to accelerate decision-making and incident response times. It allows companies to benefit from real-time insights and relieves the IT burden on centralized data centers. Processing data at the edge means that the device doesn't send large amounts of raw data to central servers—it only sends the information and analysis needed for further use. This results in reduced latency and improved reliability. The Architecture of edge devices is therefore a particularly well-suited approach for IoT, for example, or for managing critical situations.

Now you know what is edge computing architecture. In the next section, we introduce the main components of an edge architecture to help you better understand how this type of network works.

Key Components of Edge Architecture 

Edge network topology includes various components that work together to process data at the edge. Components of edge architecture are:

1. Edge Device: The edge device is actually the main device that is supposed to generate information. For example, a smartwatch that measures your heart rate is considered an edge device. Thermometer sensors and cameras are other examples of edge devices.
2. Gateways/nodes: Edge nodes and gateways are placed between the edge device and the network. These gateways perform basic processing and then filter the data so that only the necessary data is sent to the Internet or the processing center.
3. Edge Platform: This part is actually the brain of an edge architecture that is installed as an application layer to perform processing, analyze data, and generally perform intelligent operations. For example, a facial recognition camera, after collecting data, sends it to the edge platform through a gateway to make key decisions using the Application layer.
4. Connectivity Layer: As its name suggests, the connectivity layer is responsible for establishing connections between different components and devices. This layer can be Wi-Fi or a mobile network such as 4G or 3G. Without the connectivity layer, edge devices in an environment cannot communicate with each other or send information to the central server.
5. Management Layer: This layer enables the network administrator to manage various edge devices within an environment. For example, in a smart factory, an engineer can see with a dashboard which sensors are not working and need to be replaced.
6. Cloud Layer: Finally, sometimes it is necessary to send some of the processed information or results to a cloud server. For example, when we want to store data for a long time or run more complex AI models in the cloud. Of course, as mentioned, not all edge networks need a cloud layer.

Although many edge networks have elements of edge architecture, certain networks may not have certain layers, like the cloud layer, because of how simple the activities are, and in other networks where security is crucial, a layer, like the security layer, may be added.

For more complex workloads or when edge resources are limited, many businesses choose to offload data to a Cloud VPS for scalable and secure processing.

Edge Deployment Models 

Although there is no limit to the type and combination of edge network deployments, if we want to categorize them based on processing power and capabilities, we should mention the following three groups.

Sensor Edge

Edge sensors act as triggers for data collection or event monitoring and sending. These sensors are optimized for a specific use, with basic built-in functionality. They typically have little or no processing power and can only communicate with the edge network, the cloud, or the data center. Examples include clocks, surveillance cameras, industrial controllers, and time-series databases.

Device Edge

Edge devices are located at the end of the network and typically have limited power, computing capabilities, and storage capabilities. They rely on a gateway to collect and process data and are typically located close to computing resources to reduce latency, bandwidth requirements, and communication issues. The device edge is often used in remote locations where a data center is not feasible (e.g., in wind turbines, on oil rigs, and in locations subject to extreme weather conditions).

Mobile Edge

Mobile services are distributed over networks positioned close to the customer for optimal operation. Mobile edge computing (MEC), or multi-access edge computing, is a highly distributed network located at the edge of the network that integrates computing, storage, and networking resources into base stations to deploy applications and services closer to mobile users.

This allows service providers to move workloads from the cloud to local servers to deliver a better user experience and reduce network latency and congestion.

Edge vs Cloud vs Hybrid (Fog) Architectures

Model	Where Processing Happens	Latency	Best Use Case Example
Cloud	Remote data centers (AWS, Azure, etc.)	Higher latency	E-commerce analytics and scalable storage
Edge	At or near the data source (IoT devices)	Very low latency	Real-time AI in autonomous vehicles or drones
Fog	Between edge and cloud (local gateways)	Medium latency	Smart factories with multiple IoT sensors

Let’s take a look at edge vs cloud computing. Because edge computing is based on a distributed computing architecture, it is sometimes used interchangeably or confused with cloud computing or fog computing. While these three approaches share some similarities, such as their distributed architecture and the placement of storage and computing resources closer to the data's origin, they are not the same thing.

Cloud Computing vs Edge Computing

Cloud computing is made possible by a massive collection of servers located around the world. When you use AWS, for example, your data is stored and processed in one of their data centers instead of your own on-premises infrastructure. Cloud providers offer a variety of services, but the problem is that even the closest cloud center may be hundreds of kilometers away from where the data is collected.

Conversely, edge computing processes data at the edge of the network. Unlike cloud computing, edge computing allows data to be processed locally, as close as possible to the source. This results in reduced latency and improved reliability.

Fog (Hybrid) Computing

But what if we could combine the benefits of cloud computing and edge computing? This is where Fog Computing comes into play. Fog Computing means placing processing and storage resources somewhere between the cloud and the edge, where they are neither too far away nor confined to a specific point. For example, suppose you have a factory with a bunch of sensors; in such a situation, the edge technology does not have enough power to process all the information, and cloud processing may also consume a lot of bandwidth. In this situation, you can use a local server in your factory that sits between the edge computing and the cloud. Thus, Fog Computing reduces latency and increases processing speed.

Edge computing use cases

Edge computing use cases are expanding in almost every industry IoT devices often use edge computing for their most basic functions. Here are some other examples of edge computing:

1- Manufacturing

Edge computing facilitates the manufacturing process because edge devices can provide information to machines, robots, and users quickly and without using a lot of bandwidth. For example, scanners can be used to check the status of a vehicle being built as it moves along an assembly line. Users can leverage this information to improve processes and make them safer.

2- Healthcare

Edge computing plays a major role in the healthcare system, as much of patient care depends on readily available information. Edge devices are used to instantly transmit data regarding patients' vital signs, allowing doctors and nurses to make important decisions quickly and accurately.

3- Transportation

The transportation sector benefits greatly from edge computing due to the proliferation of useful information that vehicles and drivers can use to improve safety and the experience of travelers and drivers. Vehicles equipped with autonomous driving technology can gather information from their surroundings and other vehicles and use it to make decisions. Some of the data they collect and use comes from the cloud or is sent to the cloud, while other data is processed at the edge.

4- Agriculture

The agricultural industry is leveraging edge computing to improve data processing while reducing bandwidth requirements to improve how crops are grown, cared for, and harvested. Additionally, data regarding the health and performance of animals, such as dairy cows, can be processed to better inform production expectations, animal care, and the management of energy resources that support the operation.

5- Telecommunications

Telecommunications have been, and will likely continue to be, one of the primary beneficiaries and providers of edge computing. As telecom companies help businesses build networks, they leverage edge computing topologies to enable a wide range of devices to connect to the organization's network and operate near its edge. Everything from virtual reality headsets to gaming devices to IoT devices on production sites interacts with edge computing topologies configured by telecoms.

Furthermore, a telecom company can set up a distributed cloud that connects a series of on-premises servers designed to support complex edge computing configurations.

What are the benefits of edge computing?

Edge computing can improve the speed at which applications process data, making instant computing convenient for end users. But these are not the only benefits of edge computing architecture; here they are:

• Minimizing Latency: While many processes can function properly with the resulting delay, some are so urgent that an edge computing architecture is required to support them.
• Reduce bandwidth requirements: For example, with an edge computing configuration within a vehicle, the edge computing infrastructure can collect data from GPS (Global Positioning System) devices, traffic lights, and other vehicles to enhance the driver experience, improve safety, and optimize fuel consumption.
• Real-time processing applications: Some processes require real-time processing to perform their most basic functions. With edge computing, this can be done instantly, improving safety for the driver and others.
• Reduced costs: By reducing the amount of information that must be transmitted over the internet, an organization may not have to use as much bandwidth. As a result, they may be able to reduce the amount they spend each month paying their Internet Service Provider (ISP).
• Smart Applications: Smart applications can recognize patterns in the environment of the edge devices they operate on, then use this information to adjust their operation and the services they provide.
• Data Privacy: With edge computing, you can improve data privacy by limiting the flow of data between the edge device and where it is processed and stored locally.

Challenges in Edge Architecture Design

Edge computing also has some significant drawbacks, including:

Limited Capacity

Many edge devices lack the power needed to perform complex computing. A cell phone, for example, while powerful compared to what was produced decades ago, pales in comparison to even a mid-range laptop in terms of power. The capabilities of a data center can still overwhelm the potential of most edge devices.

Network Connectivity

Connectivity dependency is an inherent flaw in all edge topologies. The infrastructure that supports many edge devices is still based in cloud data centers. If the connection between the edge device or network and the cloud is lost, the topology may not function at all.

Edge infrastructure design 

A company's remote devices can number in the thousands or more, and each device entails increased maintenance and management requirements. This means an increased burden of software updates, deployments, provisioning, and monitoring.

Storage Efficiency

With remote devices having limited computing and storage resources, IT departments sometimes have to determine which data should be stored and processed locally and which should be sent to on-premises or cloud servers.

On top of these, if an edge device loses its connection to the computing resources that support it, in many cases, it could be rendered useless. This applies even to edge configurations that do not rely on the internet for operation.

 The Future of Edge Architecture 

According to IDC’s Edge Computing Spending Guide, global edge spending is expected to reach $274 billion by 2025, and is expected to continue growing at a compound annual growth rate of 15.6 percent. The evolution of AI, IoT, and 5G is accelerating the adoption of edge computing worldwide, with the number of use cases and workloads deployed at the edge increasing significantly. Edge systems will likely be more widely deployed in smart cities, autonomous vehicles, healthcare, and industry to reduce latency, increase data security, and reduce cloud dependency. In addition, the combination of edge computing with AI will enable decisions to be made in real time, which will revolutionize system performance.
 
Conclusion 

Now you know what edge computing architecture is exactly. Edge computing will become increasingly important as the world moves toward an increasingly connected and digital future. As businesses continue to adopt this technology, you will see increased investment in edge computing solutions from both companies and vendors to leverage its potential. We can also expect technological advancements that will further enhance the capabilities of edge computing solutions.

Category: Technology