The exponential growth of data collected by billions of mobile devices and the IoT is driving a shift from sending data to the cloud for processing and storage to a distributed model where some of the computing takes place at the edge. From the network, closer to where the data is created.
What is edge computing?
Edge computing refers to the processing, analysis, and storage of data closer to where it is generated to enable faster near-real-time analysis and responses. Some companies have consolidated operations by centralizing data storage and cloud computing in recent years. However, the demands of new use cases enabled by billions of distributed devices, from advanced warehouse and inventory management solutions to vision-enhanced robotic manufacturing chains and traffic control systems in smart cities, have meant that this model is unsustainable.
In addition, the increasing use of edge devices, from the Internet of Things (IoT) devices such as smart cameras, mobile point of sale kiosks, medical sensors, and industrial computers, to IT infrastructure and gateways, in search of Faster, actionable information in near real-time at the data source drives near exponential growth in the amount of data generated and collected.
It is estimated that by 2025 75% of data will be created outside of central data centers, where most of the processing takes place today.
1 Going one step further, approximately 90% of all data collected by businesses today will never be used.
2 Edge computing provides a way to reap the benefits of the data that is collected on devices through high-performance processing, low-latency connectivity, and secure platforms.
What drives edge computing?
Cloud computing is being pushed to the limit 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 is not enough to provide the requirements to process data from connected devices faster and generate immediate information and action in near real-time. These loopholes drive the adoption and use of edge computing.
Key factors contributing to cloud challenges include, but are not limited to:
Latency. More and more industries implement applications that require quick analysis and responses. Unfortunately, cloud computing alone cannot keep up with these demands, as latency due to network distance from data sources leads to inefficiencies, lag times, and poor customer experiences.
Bandwidth. Adding transmission bandwidth or more computing power could solve latency problems. However, as businesses continue to increase the number of edge devices on their network and the amount of data they generate, the cost of sending data to the cloud can reach impractical levels that can be alleviated if the data can be processed saved, and analyzed at the edge.
Security and privacy. Securing confidential information, such as private medical records, at the edge and transmitting less data over the Internet could help increase security by reducing the risk of interceptions. Additionally, some governments or customers may require that the data remain in the jurisdiction in which it was created. In healthcare, for example, there may even be local or regional requirements that limit the storage or transmission of personal data.
A persistent lack of Internet connectivity can hamper cloud computing, but various network connectivity options make edge-to-cloud computing feasible. For example, 5G offers a high-bandwidth, low-latency connection for fast data transfer and service delivery from the edge.
IA . With the need for actionable intelligence in near real-time, companies need AI at the data source to enable faster processing and to take advantage of the potential in previously untapped data.
Advantages of edge computing
Moving some data functions such as storage, processing, and analytics from the cloud to the edge and closer to where the data is generated offers several key benefits:
Higher speed and lower latency. Moving data processing and analysis to the edge helps accelerate system response, enabling faster transactions and better experiences that could be vital in near-real-time applications such as autonomous vehicle operation.
Improved management of network traffic. Minimizing the amount of data sent over the network to the cloud can reduce bandwidth and the costs of transmitting and storing large volumes of data.
Greater reliability. The amount of data that can be transmitted over a network at any one time is limited. In locations with poor quality internet connectivity, storing and processing data at the edge improves reliability when the connection to the cloud is interrupted.
Improved security. An edge computing solution can increase data security by limiting data transmission over the Internet with proper implementation.
From the edge to the cloud
Although edge computing offers organizations an unprecedented opportunity to unlock the value of data, the cloud remains essential as a central data repository and processing center. The following image shows how edge devices for data collection, computing, storage, and networking combine to help organizations get the most out of their data at every point.
Boosters for the edge
Edge computing and IoT devices collect data and manage it in one of two main ways. Intelligent edge computing devices with embedded processors can offer capabilities such as embedded analytics or AI, while non-processor devices send the data they generate to an on-site edge deployed server for storage and analysis. An on-premises edge server can then process the data from edge computing devices and return important information needed for applications in near real-time or send only the relevant parts of the data to the cloud. Data from many edge computing devices can be consolidated in the cloud for further processing and analysis.
Edge Computing Case Studies
Intel has worked with many industry partners and end customers to implement tens of thousands of edge computing solutions. Here are four case studies 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 improve the efficiency of supply chains and product development. Additionally, edge computing can support near real-time customer behavior analysis for an improved and potentially more secure shopping experience.
For example, Sensormatic’s video-based AI solution helps retailers open their stores safely during the COVID-19 pandemic by tracking capacity and monitoring social distance.
Industry: Edge computing can provide 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 automotive manufacturing engineers to create a flexible and scalable platform that uses predictive analytics and machine learning algorithms to increase weld inspections and improve critical quality control processes.
Education – Some software-based education solutions use on-device AI for personalized virtual assistance, natural language interaction, and even augmented reality experiences. For example, the ViewSonic digital whiteboard experience uses vision and edge technology to recreate the classroom experience for students and teachers involved in distance learning.
Healthcare: Edge computing can help transform outcomes with inpatient and outpatient monitoring and telehealth services and use machine learning and deep learning inference in imaging equipment to help detect health problems faster. Philips improved medical imaging inference AI by 188% on existing CT equipment without the need to purchase expensive new hardware.
Edge computing technology application
In Episode 5 of “Inside the Edge,” Steen Graham , Intel CEO of IoT, takes a look at real-world applications of edge computing, from healthcare to manufacturing to retail, to demonstrate how solutions Edge computing with Intel® technology can enable new customer experiences and revolutionize entire industries.
Best results start at the edge.
Edge computing offers an unprecedented opportunity for businesses and service providers to unlock the value in data. With the right partner, a business can get the most out of data at every point. With tens of thousands of true-value edge implementations, hundreds of ready-to-market solutions, standards-based technology, and the world’s most mature developer ecosystem.
