What Is Edge Computing? How Edge Computing Works

Edited by Lizzy Schinkel, Tech Writer | Reviewed by Brian Gilbert, Network Administrator

Cloud computing has been around for several decades now, revolutionizing how people store and use data. However, it has limits. To solve bandwidth, latency, and offline issues, users can turn to edge computing instead.
Edge computing is a new way of processing data in real time. Learn what edge computing is, how it works, and how to differentiate it from cloud computing.

What is edge computing?

Edge computing is a distributed computing paradigm that brings data processing closer to the sources of data. Instead of relying solely on a centralized data server to do all the work, edge computing moves the computation as close to the edge of the network as possible.

In simple terms, edge computing crunches data right where it's collected. That means fewer processes run in the cloud. Processing mainly happens in local places, like on your phone, mobile device, computer, or Internet of Things (IoT) device.

Processing data locally minimizes sending large amounts of data to the cloud. This means that the main goal of edge computing is to reduce bandwidth requirements while saving network costs.

When data physically exists closer to the devices, gateways, or users who connect to it, it allows users to quickly and securely share data without latency.

Devices such as routers, gateways, and IoT-enabled devices are some edge devices that do edge computing daily. These devices have computing resources and can perform tasks locally without the need for constant Internet connectivity.

Many IoT applications require immediate responses, like automated systems in factories or self-driving cars. Edge makes a lot of sense in such applications where latency is critical.

Because information is power, organizations that effectively use data to gain insights, make informed decisions, and optimize operations have a significant competitive advantage. However, the velocity of data being generated can be overwhelming. Traditional cloud computing struggles to handle the real-time nature of this data.

Network limitations like latency issues collectively hinder data management efforts. In response to data challenges, modern businesses are shifting edge computing architecture. Today, edge computing is not only reshaping business computing, but also information technology as a whole.

How does edge computing work?

Traditionally, when your phone or device collects data, it sends it to a central data center to be processed. This can cause delays, especially when lots of devices are sharing data at the same time.

Edge computing changes that. It moves data processing and storage closer to where the data is created. This helps fix three major network issues: bandwidth, latency, and congestion.

• Bandwidth is the amount of data that can be sent over a connection in a certain time. It’s measured in bits per second.
• Latency is the delay between sending data and getting a response.
• Congestion happens when too much data tries to move through the network at once, causing slowdowns.

With edge computing, data starts at devices like sensors, IoT tools, or edge servers. These devices gather information from their surroundings.

Instead of sending everything to a faraway data center, edge devices process the data right there, nearby. They sort out what's useful, do simple analysis, or even run machine learning models to get insights.

These devices can also store important data locally, so they don’t have to send everything to the cloud all the time. That makes it faster and reduces network traffic.

One of the biggest benefits of edge computing is real-time decision making. Since the data is stored and processed close by, edge devices can act on it right away without waiting for instructions from far away.

Edge computing doesn’t replace the cloud. It works with the cloud. Many systems use both; for example, by analyzing data at the edge first, and then sending summaries to the cloud to train AI models.

Why is edge computing important?

Edge computing is a big part of today’s tech systems. It brings several key benefits:

• Lower latency. Data doesn’t have to travel far to reach a cloud data center. Edge computing brings the cloud closer, so things happen faster. This is critical for real-time tasks like self-driving cars or factory machines.
• Saves bandwidth. Since edge computing processes data where it’s created, only the most important data is sent to the cloud. This cuts down on how much data travels across the network.
• Better security. Sensitive information stays closer to the source instead of traveling to far-off servers. This lowers the risk of data getting stolen, which is especially helpful for industries handling private info.
• More reliability. If the internet goes down, edge systems can still work. They don’t rely on always being connected to a central server. This is useful in remote areas where connections are spotty.
• Easy to scale. As more smart devices come online, edge computing helps handle all that data without putting too much pressure on cloud systems. It grows as your needs grow.
• Lower costs. Processing data at the edge means less need for expensive cloud storage and transmission. Businesses can save money by using local computing power.
• Great for IoT. Edge computing works well with devices like sensors and smart tools. It lets them respond quickly and manage data right where it’s made.

What are examples of edge devices?

Edge computing works by using devices that can process data on their own. These edge devices collect and analyze data right where it's made - at the "edge" of the network - so there’s no need to send everything to a far-off data center.

Here are some common types of edge devices:

• IoT sensors. These devices gather data from the world around them—like temperature sensors, smart home gadgets, or machines in factories.
• Smart applications. Devices like smart meters and thermostats don’t just collect data—they also process it on the spot.
• Gateways. Gateways help connect edge devices to the cloud. They gather local data and only send what’s needed for deeper analysis.
• Routers. Routers help edge devices talk to each other and the cloud. They’re a key part of how information flows in an edge network.

Edge vs. cloud computing

Cloud computing and edge computing are related concepts with distinct characteristics. While there is some overlap in their functionalities, they aren't interchangeable terms.

Both are related in that they all involve processing data in distributed computing environments. The key difference, however, lies in the location of the resources within the network architecture.

Edge computing

Edge computing handles data close to where it’s created. Instead of sending all information to a faraway cloud, edge devices, like local servers or smart sensors, process the data nearby. This speeds things up and cuts down on lag.

For example, a wind turbine might have its own server that reads and analyzes data from sensors right on the spot. Or a train station might use nearby computers to monitor and respond to rail traffic in real time.

After the edge devices process the data, the results can be sent to a main data center for deeper analysis, storage, or combining with other information.

Cloud computing

Cloud computing is the on-demand delivery of computing services like servers, storage, or databases over the Internet rather than having them on your physical devices. Unlike edge, the cloud prefers using remote data centers for storage.

Users can access resources remotely through the Internet via a web browser most of the time. You pay only for the resources you use. Therefore, there's no large upfront investments in hardware and software.

In practice, cloud computing substitutes traditional data centers. It has been deployed to unlock the full potential of the Internet of Things in the past few years. Cloud platforms offer a framework for developing and deploying IoT applications seamlessly. While it analyzes vast amounts of data generated by IoT devices, the edge handles on-device processing and real-time actions.

However, cloud computing has shortcomings. It introduces latency due to the physical distance between users and the data centers where cloud services are hosted.

What is fog computing?

Like edge computing, fog computing brings cloud services closer to where data is created. It works as an extension of the edge, but spreads across a wider area.

Fog computing uses many "fog nodes" to handle local data processing and storage. These nodes sit between edge devices and the main cloud, helping reduce delays and improve speed.

Although it’s still a developing technology, fog computing shows strong promise in fields like transportation, healthcare, and smart cities.

Edge computing use cases

Ideally, edge computing puts computation and storage at the same point as the data source at the network edge. Here are some examples of edge computing in action.

• Industrial Internet of Things. In factories, sensors collect data on machine performance, temperature, and other factors. Edge computing allows this data to be processed and analyzed locally, thus enabling real-time decision-making.
• Smart cities. Traffic cameras process video locally to detect congestion and optimize traffic flow in cities. That way, it's easier to adjust traffic lights or send alerts about pollution levels.
• Autonomous cars. Edge computing is also crucial for the successful deployment of autonomous vehicles. The cars generate massive amounts of data that need quick processing without waiting for instructions from a server to heighten passenger safety.
• Retail. Retailers use edge computing to do data analysis regarding the stores. This data improves store layout, optimizes pricing, and personalizes the shopping experience.
• Healthcare. Wearable health monitors check heart rate and other biometrics on-device, providing immediate alerts to healthcare providers.

Challenges of edge computing

Although this type of computing offers many advantages, it's fairly far from being foolproof. It presents certain challenges, including:

• Security concerns. Edge computing can increase attack factors. The addition of IoT devices in the mix creates new opportunities for cyber threats, as most of them are notoriously insecure. Edge devices also operate in distributed and uncontrolled environments.
• Deployment issues. Deploying edge computing infrastructure across geographically dispersed locations can be complex. The technology is costly as well because it requires extra local hardware.
• Incomplete data. It refers to situations where edge devices cannot access the full dataset required for accurate analysis. This can occur due to various reasons, including intermittent connectivity and limited storage capacity.

Mitigating these challenges requires a holistic approach that combines technical innovation, robust architecture design, and effective management practices.

The emergence of new technologies, like the 5G network, unlocks edge computing's potential. It guarantees data transmission at the edge to enable devices to make autonomous decisions.

Frequently asked questions

What is network edge?

A network edge refers to the boundary or periphery of a network where data enters or exits the network. It's essentially the meeting point between your private network and the wider world.

What is mobile edge computing?

Mobile edge computing by definition is the processing of data produced by edge devices and applications close to the point of capture. Essentially, it extends the edge of your network.

Where is edge in edge computing?

The edge refers to the central location where data is generated and processed, typically at or near the source of data production.

What is an example of edge computing?

An example of edge computing is the use of a smart sensor in a factory to analyze data locally on temperature. It detects overheating before cloud analysis, preventing machine failure in milliseconds.