Edge computing helps organizations process and store data quickly and locally. Learn about the edge, its benefits, and use cases.
Much like cloud computing in the mid-2000s, edge computing has become one of the biggest buzzwords in the business world. And much like the cloud promised to revolutionize technology for both businesses and consumers, the edge will similarly change how we use and access tech.
Whereas cloud computing sends data back to a centralized location for processing, edge computing allows for data to be processed and analyzed closer to the source of that data.
That typically means the data can be processed more quickly and efficiently, and the amount of data that needs to be transmitted over networks can be reduced. Especially as the increasing use of IoT (Internet of Things) and other connected devices continues to grow, edge computing has become more and more important for both businesses and consumers.
Along with processing data from IoT devices—which are projected to make up 75% of all devices by 2025—edge computing allows businesses to more deeply and efficiently analyze big data and allows developers to create more intelligent, increasingly responsive technology powered by AI (artificial intelligence).
Keep reading to learn more about edge computing, its benefits and use cases and how it differs from cloud computing.
Edge computing and cloud computing both involve the processing and storage of data. However, they differ in terms of where that processing and storage takes place.
For example, let’s imagine internet access as a circle. You can think of cloud servers as living at the center of that circle, and edge servers as existing at different points along the edge of the circle. With cloud computing, no matter where data is sent from, it will be processed and stored at the center of the circle. Under an edge computing model, data gets sent to the point of presence (PoP) closest to the point from which it’s sent.
Now imagine a device situated in a remote location somewhere outside the circle—like an IoT sensor monitoring a wind turbine in the middle of a field, for example. To send data to the center of the circle under a cloud computing model would take far too long for that data to be useful. By the time the sensor data was processed, an alert for something like a simple maintenance issue could have escalated to system-wide failure. By sending it to the PoP closest to the edge, data can be processed quickly enough to be acted upon.
Below, we’ll explore more differences between edge and cloud computing and how each of them can be used for different purposes.
Cloud computing refers to the processing and storage of data in centralized locations, typically in data centers owned and operated by cloud providers—think Amazon Web Services (AWS) and Microsoft Azure. Data gets sent over the internet to these data centers for processing and storage, sometimes over very long distances depending on where you are in relation to a data center.
Due to these longer distances, cloud computing can process data less quickly than edge computing. Depending on the situation, this delay in processing and analysis might not matter much to consumers. However, for scenarios where real-time analysis is critical—such as an autonomous vehicle speeding down the highway—the latency of cloud computing is less than ideal.
However, processing and storing data in central locations means that cloud providers can give customers access to shared pools of configurable computing resources. Sharing resources in this way creates a scalable, flexible computing solution.
Edge computing refers to the processing and storage of data at the edge of the network, or as close as possible to the source of the data. These data sources can include devices such as smartphones, IoT sensors and cameras, as well as gateways and other edge devices connected to these devices.
Sending data to the edge instead of to a central server means it can be processed at a lower latency. With faster processing, you can get near-instant feedback and analysis. So edge computing can be more advantageous when you require low latency, high security and low bandwidth.
Cloud and edge computing have their own sets of advantages for different applications. Below, we’ll explore some of the benefits of edge computing.
Edge computing allows for faster data processing by reducing the distance the data needs to travel. By processing data closer to the source, edge computing can reduce the time it takes for data to be analyzed and acted upon. Faster processing is important in applications such as autonomous vehicles, drones and industrial IoT where low latency is crucial for fast decision-making.
Edge computing can be used to process data locally when network connectivity is unreliable or expensive. Local processing helps avoid the need to transmit large amounts of data to a cloud-based service.
By keeping data local and processing it on an edge device, edge computing can help to ensure that data is still processed and analyzed even when network connectivity is lost. That means that even if there’s no network connection, some edge devices will store and process data locally. Once a network connection is re-established, the edge device can then transmit the stored data to a centralized location for further analysis, if needed.
By keeping data closer to the source, edge computing can help reduce the risk of data breaches. For example, edge computing keeps personal and sensitive data on-premise. By not sending data to the cloud, edge computing can help organizations comply with data privacy regulations.
In situations where bandwidth is limited, edge computing can help reduce the amount of data that needs to be transmitted over networks.
Edge computing can be more cost-effective than cloud computing in situations where the cost of transmitting large amounts of data to a cloud-based service would be prohibitively expensive.
Edge computing can handle the high volume, velocity and variety of data generated by IoT devices more capably than cloud computing. It also enables those devices to work offline.
Overall, edge computing can help organizations to improve the speed, efficiency and security of data processing, as well as reduce the amount of data that needs to be transmitted over networks, making it a valuable addition to the overall computing infrastructure.
Edge computing is still developing, and organizations are figuring out how to optimize the edge, the cloud and hybrid models for best results. Before migrating all your operations to the edge, it’s important to keep several factors in mind.
Edge computing involves deploying and maintaining a large number of edge devices. As such, it can be more complex to set up and manage than cloud computing. However, as the edge becomes more popular, more providers are popping up to demystify the edge space and give customers simplified options to access their edge operations.
Edge devices are generally less powerful than cloud-based servers, which can reduce the types of computations that can be performed on the edge. Using a hybrid model and keeping some operations in the cloud is one way to counteract this limitation.
Edge computing is also bounded by the finite number of devices and resources available at the edge. If you’re setting up your own edge devices, this limitation can make it difficult to scale as needed. However, as more providers offer access to the edge, it should become easier for organizations to scale their edge operations.
Fewer edge providers also mean that choosing edge services can feel more scant than choosing a cloud provider. There are currently fewer options on the market, which can make it more difficult to switch between different services and providers as needed. However, that market is expected to grow to $156 billion by 2030, so we should see more options cropping up as the technology continues to develop.
Edge devices require regular maintenance and updates to keep them running smoothly. If you’re maintaining your own edge operations, the upkeep can be more time-consuming and costly than maintaining cloud-based services.
Cloud computing is vulnerable to security breaches because there’s a central point of access that can take out all your operations at once. However, you can think of security for cloud services as putting all your operations into one huge, heavily monitored fort. All the top security teams are located there, and there are emergency teams at the ready to take out a threat when the fort is targeted.
Edge computing is more like many smaller forts dotting the frontier. Security on the edge is locally effective, as we discussed in the benefits section. However, more edge devices also provide more points of attack, giving bad actors more targets and entry points into your network. Unlike a cloud breach, an edge breach won’t take out half the internet if services go down. But edge devices, especially if they aren’t properly configured and maintained, can give cybercriminals more options to wreak havoc—on a smaller scale—in more places.
Overall, edge computing can be complex, resource-intensive and less flexible than cloud computing. The technology might not always be the best choice for certain types of applications and workloads. It's important to carefully evaluate the requirements of a specific use case and decide whether edge computing or cloud computing is the best fit for your organization.
There’s a wide range of use cases for edge computing and many situations where the edge is the obvious choice over the cloud. Below, we’ll take a look at several common, popular and emerging use cases for edge computing.
The buzz around edge computing grew along with the hype around the release of 5G in 2019. Edge computing allows for low-latency operations, which means that it can be used in conjunction with 5G networks to support low-latency, high-bandwidth use cases such as augmented reality (AR), virtual reality (VR) and gaming.
With its low latency, edge computing can reduce delays in communication over the internet, making it seem like it’s happening closer to real time. Of course, the lack of delays can improve the enjoyability of your video call. But it also has implications for tools like conversational AI, which can help companies serve customers more quickly and reliably with virtual agents.
Autonomous vehicles can use edge computing to process data from cameras, lidar and other sensors. The high speed of processing at the edge allows for real-time decision-making and control, which is critical whether your autonomous vehicle is cruising through residential streets or speeding down the highway.
Real-time monitoring and control is also critical for industrial applications. Edge computing can be used to process and analyze sensor data from industrial equipment and machinery. These sensors can often run quality control checks, for example. If something seems off, data from the sensor can alert a maintenance team in real time, allowing them to fix an issue before it causes huge problems.
Because of its spread-out nature, edge computing can also be used to process sensor data from remote monitoring devices, allowing for real-time monitoring of remote locations such as oil rigs, wind turbines or mining operations.
You can also use these monitoring capabilities at an individual level, especially in healthcare. Sensor data from health devices, such as wearable devices, implantable devices and remote monitoring devices can be processed and analyzed quickly at the edge. This allows for real-time monitoring of different vital signs, such as heart rate, blood pressure or blood sugar levels.
This quick, constant analysis can help reduce the need for patients to visit the hospital or clinic for regular check-ups. Especially for patients with chronic conditions or for those who live in remote areas, the ability to receive real-time alerts or have them sent to their healthcare providers can be life-changing.
Edge computing can be used for content delivery by placing content delivery networks (CDNs) and caching servers at the edge of the network, closer to the end users. This more local placement allows for faster content delivery since the data doesn’t need to travel as far to reach the user.
Additionally, edge computing can also be used for real-time user data processing, such as for personalized content recommendations or for dynamic ad placement. This quick delivery can be accomplished by running applications and services on edge devices—such as gateways, routers and other IoT devices—rather than in a centralized data center or cloud.
Smart home devices like thermostats, security cameras or smart speakers can use edge computing to process data locally instead of sending it to a centralized cloud server. Local processing can reduce latency and improve responsiveness.
Real-time monitoring might not be as necessary for smart home devices as for something like an implanted glucose monitor. However, local processing also reduces the amount of data sent to the cloud from something like your smart speaker, freeing up bandwidth for more critical operations.
Paired with other emerging tech such as 5G and IoT devices, the capabilities of and demand for edge computing continues to increase. Other new and emerging technologies like AI and blockchain make edge computing more desirable, allowing for real-time data processing and increased security.
As edge computing continues to grow, we’ll see increased innovation and capabilities, especially when it comes to high-speed data analysis and real-time communication.
Telnyx is a next-generation communications platform that has been innovating in the connectivity space for over a decade.
Telnyx has spent a decade building a global IP network with 10 multi-cloud PoPs in major metro areas across the globe. This powerful network, combined with our status as a carrier in over 30 countries, puts Telnyx in a unique position to offer complete connectivity, wireless and data solutions for customers looking to build both distributed and hyperlocal services.
Historically, developers have had to navigate a web of service providers or aggregators to get access to global connectivity and infrastructure. With Telnyx’s local PoPs and APIs, customers have an improved, secure way to access the processing powers of the cloud.
Telnyx puts builders in the drivers seat. With our self-service Mission Control Portal and low-latency APIs, Telnyx makes it easy for developers to own, manage and scale their connectivity and infrastructure.
As a global carrier that has built its own global IP network, Telnyx offers customers industry expertise and award-winning customer support. By sharing our knowledge, we allow developers to focus on building and scaling their business without requiring deep investment in infrastructure, operations or compliance.
Telnyx’s multi-cloud, private IP network delivers consistent reliability and resilience, giving customers confidence that they’re building on top of scalable, enterprise-grade infrastructure. With our Virtual Cross Connects and private network, we ensure that our customers can access the network at the edge and traverse the internet, securely connecting disparate cloud environments.
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