Edge computing solutions1/1/2024 ![]() ![]() 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. 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: 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. For example, 5G provides a high-bandwidth, low-latency connection for rapid data transfer and service delivery from the edge. Lack of persistent internet connectivity can impede cloud computing, but a variety of network connectivity options make edge-to-cloud computing feasible. In healthcare, for example, there may even be local or regional requirements to limit the storage or transmission of personal data. In addition, some governments or customers may require that data remain in the jurisdiction where it was created. 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. 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. Adding transmission bandwidth or more processing power could overcome latency issues. 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. More industries are implementing applications that require rapid analysis and response. Key contributing factors to challenges in the cloud include: These gaps are driving the adoption and use of edge computing. 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. 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.Ĭloud 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. 1 Taking this a step further, approximately 90 percent of all data collected by enterprises today will never be used. It’s estimated that by 2025, 75 percent of data will be created outside of central data centers, where most processing takes place today. 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.Īdditionally, 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. In recent years, some companies have consolidated operations by centralizing data storage and computing in the cloud. Edge computing refers to processing, analyzing, and storing data closer to where it is generated to enable rapid, near real-time analysis and response. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |