Everything You Want To Know About Edge Computing
Edge computing offers a solution to network traffic congestion and reduces latency and bandwidth usage.
Jan 20,2023 January 20,2023
These days it’s hard to imagine life without the world wide web. Every year, more devices are added to the internet, congesting network traffic and forcing companies that rely on real-time data transfer to adapt and develop new ways to combat latency. One such idea that proved very useful is edge computing.
What Is Edge Computing?
Edge computing is a way for businesses to combat the ever-growing problems of latency, reliability, and high bandwidth usage on the global network.
This is done by reducing the amount of traffic between the edge device and the cloud server or other centralized data center by running certain processes on the edge device and only sending the most necessary data to the cloud server. That would be a textbook definition of edge computing, but to fully grasp the concept, let us explain what edge devices are.
An edge device (sometimes called an edge server) got its name due to its location on the network when viewed in a 2D plane. Any device that communicates to the endpoint devices can be considered as being on the edge of the network. The most common examples would be routers, Wi-Fi adapters, or switches.
But to be considered an edge device, the device should be able to do more than just collect and transmit data: it should also be capable of processing, monitoring, and filtering it. Due to its proximity to the endpoint device, it minimizes latency, improves traffic reliability, and uses a lot less bandwidth than a regular connection to the cloud server would.
How Does Edge Computing Work?
Before we explain the edge computing network, let us first look at how the cloud computing network works so you can see the differences more clearly.
Cloud computing is a centralized computing solution where the company gets access to IT resources without having to invest in building its own IT infrastructure. A cloud-based service handles everything from data storage to data processing.
As you can see, the cloud isn’t a magical place. It’s actually just a remote server managed by someone else. In such a setup, every computer on the network is connected to the cloud server and constantly sends and receives information from the user. It responds to user data requests and executes any tools or software stored on it.
Edge computing limits the network's need to interact with the cloud server by moving the server as close as possible to the device or, simply put, closer to the edge of the network. This type of setup limits latency impact and significantly reduces network load and bandwidth usage, allowing for a near-instantaneous data transfer. As you can see, the edge device’s location is the key here.
A good edge computing example would be a remote weather monitoring station. Instead of the station constantly sending information to the control center, where it would be analyzed and processed, the remote monitoring station would handle the collection and processing of data and only send crucial information to the control station.
Except for introducing new devices, which possess more processing power and are more compact, there isn’t anything particularly new with edge technology. Edge computing only dictates how to set up the company's distributed computing architecture and use the already existing infrastructure.
Companies have used and continue to use conventional on-site data centers. Most modern edge computing systems are a mix of a company's own IT infrastructure and the one it rents, with a focus on making the best use of resources.
You shouldn’t mix edge computing with fog computing, which represents a mid-point between edge and cloud computing. Instead of the server being right next to the device, the fog server is located further away but not as far as a cloud server.
Let us explain with an example. A cloud server covers a state, a fog server covers a town, and an edge server covers a building. They aren’t strictly set like that, but it’s a good way to think about their differences.
Uses of Edge Computing
Edge computing has many current and potential uses in today's world. Why do we say "potential"? Because it’s not yet implemented everywhere. Most businesses don’t require near-instant communication to run their day-to-day operations. Therefore, they still rely on a cloud data center. Companies that require an almost instant transfer of data use or will use edge computing to reduce their running costs and improve their operations.
Edge computing technology can be used for:
- Autonomous vehicles
- Remote asset monitoring
- Smart power grid
- Hospital monitoring
- Traffic management
It’s estimated that by the end of 2025, there will be 2.1 million autonomous vehicles in the US alone. To be able to operate safely, all autonomous vehicles need to collect and process vast quantities of data constantly. Transferring said data to the cloud and getting the results back would take too long, as a split-second decision while driving can make the difference between life and death.
Instead of using cloud computing, the manufacturers implemented an edge computing solution and equipped the vehicles with enough processing power to process data as it is received and only communicate with the cloud server to transfer non-essential information like the GPS location and weather reports.
Remote Asset Monitoring
While having a constant uplink of data and a video stream from a remote asset like a warehouse or an oil pump seems like a good idea, such data transfers aren’t lightweight.
Depending on how remote the location is, the connection can be very unreliable.
Edge computing allows data processing to occur much closer to the asset, with minimal reliance on centralized cloud servers to handle the data analysis. If there is a need for human intervention or anybody wants to check the status, the request would be handled through a remote connection or a cloud server, but most of the daily operations are done on the edge devices.
Smart Power Grid
As an increasing number of countries pledge to combat the effects of global warming and reduce their carbon footprints, the efficient use of electricity will become increasingly important to governments and companies alike.
Proper implementation of an edge computing solution can optimize electricity consumption by monitoring and analyzing data from sensors and other internet of things (IoT) devices to determine if the facilities are being used and when the peak and off-peak times occur. With the data in hand, the program can either decide how to best regulate the power or relay the final result to a human for approval.
Patient and other relevant sensor data in hospitals needs to be instantly processed and acted upon by the hospital staff. There’s no time for data to be transferred to a traditional data center for processing and then wait for results. Even if it was quick enough to do, constantly transferring such vast quantities of sensitive data would quickly congest network traffic and eat up the hospital's bandwidth.
With edge computing technology, most data processing can be done on-premises, and only necessary data is sent. This significantly reduces congestion, which is essential considering the last thing a hospital wants is for critical data to be late, even if we’re talking mere seconds.
Instead of having a centralized traffic control system that micromanages every aspect of public transportation, the city can decentralize the system by setting up edge devices everywhere. At the very least, this can be done in heavily used areas, with only the most crucial info being sent to a traditional data center. This setup would still allow traffic control to have complete control but only act when the system detects an issue.
Benefits of Edge Computing
The most significant benefits come from reduced latency and bandwidth usage but also improved network reliability. They aren’t the only benefits such a network provides, but they're its primary selling point, and most of the additional benefits revolve around them.
Moving from a centralized to a decentralized system will give edge computing systems greater autonomy and the ability to collect, manage, and store data from the end devices instead of constantly sending data to a centralized server.
Depending on the system and how it is set up, it may be able to do various things with the data it receives, such as acting on it, waiting for instructions from the server, or sending important data when the connection is established. Autonomy is extremely useful when decisions must be made immediately or when handling data in a remote place like the middle of the ocean, where internet connectivity is limited and expensive. This leads us to the next benefit.
While people often like to consider the internet as a free resource available to all where you can do pretty much anything without revealing your identity, the reality is much different. For starters, if the internet were truly anonymous, we wouldn't need VPNs or worry about their potential to be tracked.
Furthermore, the internet isn’t free. You need to pay for data transferred over the global network, also known as "bandwidth." Since edge computing devices reduce the amount of communication required with the server, the traffic is automatically reduced, which saves money in the long run.
Challenges of Implementation
As with anything else, there are some drawbacks to the implementation of edge devices, most of which are related to cost and logistics.
High Initial Cost
While edge computing saves money in the long run, the company will first have to invest in its IT infrastructure. This initial cost is what pushed most companies toward cloud computing in the first place, but business requirements and long-term savings are now pushing companies to make some compromises.
There is no beating around the bush: edge deployments can be very costly, and the company has to have a clear plan on what it will do with them, or it’ll be only throwing money away.
If the company must handle massive amounts of data, it has to have adequate data storage capacity to handle and store it safely. Depending on what the company does with the data, it can be discarded as soon as the software is done analyzing it.
That said, some data, especially in the medical field, must be stored and kept safe from prying eyes and security breaches, raising the cost of storage. It’s cheaper to upload data to the cloud and let someone else take care of it for you.
Adding more devices to the company network will only open additional attack vectors. It doesn’t help that the said devices are located close to the endpoint device and therefore don’t benefit from any layered defenses. Edge computing solutions have to be secured and properly managed to ensure the safety of company data and prevent interruption of critical processes that could cause irreversible damage.
Edge computing provides a way forward for companies that rely heavily on continuous data streams to function. With the global network continuing to expand, we expect to see more and more companies embrace the technology, either to reduce their operating costs or lay the foundation for future expansions. After all, it’s better to invest in your equipment once than to pay someone else for their services constantly.
Edge computing refers to a computer network architecture that can perform computation and data storage close to the source of the data generation. By processing data locally, the utilization of the network is severely reduced, which reduces the use of bandwidth and allows for almost instantaneous access to and use of data stored locally.
Cloud computing allows for remote handling of data collection and analysis, while edge computing does everything cloud computing does but on a local device instead of a remote one.
They are two different things, but together they have the potential to revolutionize IoT devices by combining them to bring mobile edge computing. 5G is an abbreviation for the 5th generation mobile network capable of achieving 20 Gbps speeds, while edge computing is a network solution designed to address the issues of network latency, congestion, and reliability.
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