Robotic arm in an assembly line

What Is Edge Computing?

Move faster, store more, process everything -at the edge.

Edge Computing Takeaways

  • By moving powerful edge computing closer to where data is generated, enterprises and service providers can identify new revenue opportunities, offer innovative services, and save time and money on operations.

  • Edge computing reduces data processing latency, increases response speed, and enables better network traffic management and compliance with jurisdictional requirements for security and privacy.

  • Edge computing is just one part of a distributed computing architecture and requires consideration of infrastructure, from edge devices to on-premises edge to network to cloud, when designing an interoperable edge-to-cloud solution.



What Is Edge Computing?

Edge computing refers to processing, analyzing, and storing data closer to where it is generated to enable rapid, near real-time analysis and response. In recent years, some companies have consolidated operations by centralizing data storage and computing in the cloud. But the demands of new use cases enabled by billions of distributed devices—from advanced warehouse and inventory management solutions to vision-enhanced robotic manufacturing lines to advanced smart cities traffic control systems—have made this model unsustainable.

Additionally, the increased use of edge devices—from Internet of Things (IoT) devices, such as smart cameras, mobile point-of-sale kiosks, medical sensors, and industrial PCs to gateways and computing infrastructure—for faster, near real-time actionable insights at the data source is driving exponential growth in the amount of data generated and collected.

It’s estimated that by 2025, 75 percent of data will be created outside of central data centers, where most processing takes place today.1 Taking this a step further, approximately 90 percent of all data collected by enterprises today will never be used.2 Edge computing provides a path to reap the benefits of data collected from devices through high-performance processing, low-latency connectivity, and secure platforms.

It’s estimated that by 2025, 75 percent of data will be created outside of central data centers, where most processing takes place today.1

Drivers of Edge Computing

Cloud computing is being pushed to its limits by the needs of the services and applications it supports, from data storage and processing to system responsiveness. In many cases, more bandwidth or computing power isn’t enough to deliver on the requirements to process data from connected devices more quickly and generate immediate insights and action in near real-time. These gaps are driving the adoption and use of edge computing.

Key contributing factors to challenges in the cloud include:

  • Latency. More industries are implementing applications that require rapid analysis and response. Cloud computing alone can’t keep up with these demands because of the latency introduced by network distance from the data source, resulting in inefficiency, lag time, and poor customer experiences.
  • Bandwidth. Adding transmission bandwidth or more processing power could overcome latency issues. However, as companies continue to increase the number of edge devices on their network and the amount of data they generate, the cost to send data to the cloud may reach impractical levels that could be alleviated if data can be processed, stored, and analyzed at the edge.
  • Security and privacy. Securing sensitive data, such as private medical records, at the edge and transmitting less data across the internet could help increase security by reducing the risk of interception. In addition, some governments or customers may require that data remain in the jurisdiction where it was created. In healthcare, for example, there may even be local or regional requirements to limit the storage or transmission of personal data.
  • Connectivity. Lack of persistent internet connectivity can impede cloud computing, but a variety of network connectivity options make edge-to-cloud computing feasible. For example, 5G provides a high-bandwidth, low-latency connection for rapid data transfer and service delivery from the edge.
  • AI. With the need for actionable intelligence in near real-time, companies need AI at the data source to allow faster processing and to take advantage of the potential in previously untapped data.

Benefits of Edge Computing

Moving some data functions like storage, processing, and analysis away from the cloud and to the edge and closer to where data is generated can offer several key benefits:

  • Increased speed and lower latency. Moving data processing and analysis to the edge helps speed system response, enabling faster transactions and better experiences that could be vital in near real-time applications, like autonomous vehicle operation.
  • Improved network traffic management. Minimizing the amount of data sent over the network to the cloud can reduce the bandwidth and costs of transmitting and storing large volumes of data.
  • Greater reliability. The amount of data that networks can transmit at one time is limited. For locations with subpar internet connectivity, being able to store and process data at the edge improves reliability when the cloud connection is disrupted.
  • Enhanced security. With proper implementation, an edge computing solution may increase data security by limiting the transmission of data over the internet.

From the Edge to the Cloud

Although edge computing provides an unprecedented opportunity for organizations to unlock the value in data, the cloud remains essential as a central data repository and processing center. The image below shows how edge devices for gathering data, computing, storage, and networking combine to help organizations make the most of data at each point.

IoT and edge computing devices collect data and manage it in one of two main ways. Intelligent edge computing devices with built-in processors may offer advanced capabilities like analytics or AI onboard, while devices without processors send the data they generate to a server deployed at the on-premises edge for storage and analysis. An on-premises edge server can then process data from the edge computing devices and return critical information needed for near real-time applications or send only the relevant portions of the data to the cloud. Data from numerous edge computing devices can be consolidated in the cloud for more extensive processing and analysis.

Edge Computing Use Cases

Intel has worked with many industry partners and end customers to deploy tens of thousands of edge computing solutions. Below are four edge computing use cases that show how Intel has helped companies enable new experiences and drive more-efficient operations.

Retail: Edge computing can use sensors and cameras to improve retail inventory accuracy and help make supply chains and product development more efficient. In addition, edge computing can support analysis of customer behavior in near real-time for an enhanced and potentially safer shopping experience. For instance, the Sensormatic video-based AI solution helped retailers open stores safely during the COVID-19 pandemic by tracking occupancy and monitoring social distancing.
Industrial: Edge computing can offer a foundation for Industry 4.0 by integrating digital and physical technologies for more-flexible and responsive manufacturing. For example, Intel and Nebbiolo Technologies worked with Audi auto manufacturing engineers to create a scalable, flexible platform that uses predictive analytics and machine learning algorithms to boost weld inspections and enhance critical quality-control processes.3
Education: Some software-based education solutions use on-device AI for personalized virtual assistance, natural language interaction, and even augmented reality experiences. For instance, the ViewSonic digital whiteboard experience uses edge and vision technology to re-create the classroom experience for students and teachers engaged in distance learning.
Healthcare: Edge computing can help transform outcomes with inpatient and outpatient monitoring and telehealth services and use machine and deep learning inference on imaging equipment to help detect health issues faster. Philips improved AI inference for medical images by 188 percent on existing CT scan equipment with no need for expensive new hardware.4

Edge Computing Technology in Application

In Episode 5 of “The Inside Edge,” Steen Graham, Intel IoT General Manager, provides a look into the real-world applications of edge computing, from healthcare to manufacturing to retail, to demonstrate how edge computing solutions, powered by Intel, can enable new experiences for customers and disrupt entire industries.

Better Outcomes Start at the Edge

Edge computing provides an unprecedented opportunity for enterprises and service providers to unlock the value in data. With the right partner, a company can make the most out of data at every point. Intel—with tens of thousands of edge deployments generating real value, hundreds of market-ready solutions, standards-based technology, and the world’s most mature developer ecosystem—can help you make the intelligent edge real.


Frequently Asked Questions

Edge computing refers to processing, analyzing, and storing data closer to where it is generated within a network to enable rapid, real-time analysis and response, creating the potential to monetize data, offer new services, and save time and money on operations. The five primary factors driving edge computing are latency, bandwidth, security, connectivity, and AI.

The network edge sits within your network just outside the network core and includes converging locations such as regional data centers, next generation central offices (NGCOs), fixed wireline access points, wireless access base stations, and radio access networks (RANs).

Edge cloud computing augments cloud computing with edge computing for certain types of workloads. With edge cloud computing, an edge cloud, hosted on an edge server that acts as a microdata center, extends the convenience of the cloud to edge networks by placing intelligent edge nodes closer to local devices, equipment, and resources for data collection, storage, and faster data processing, resulting in reduced latency for edge applications that depend on near real-time data.