Edge computing allows data produced by internet of things (IoT) devices to be processed closer to where it is created instead of sending it across long routes to data centers or clouds. Doing this computing closer to the edge of the network lets organizations analyze important data in near real-time – a need of organizations across many industries, including manufacturing, health care, telecommunications and finance. "In most scenarios, the presumption that everything will be in the cloud with a strong and stable fat pipe between the cloud and the edge device – that's just not realistic," says Helder Antunes, senior director of corporate strategic innovation at Cisco. Edge computing is a "mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet," according to research firm IDC. It is typically referred to in IoT use cases, where edge devices would collect data – sometimes massive amounts of it – and send it all to a data center or cloud for processing.
The following is a guest article from Dr. Rob MacInnis, CEO and founder of AetherWorks. When it comes to processing and storing data, should we expect cloud to continue to reign as the go-to option of 2017? The data suggest that this might be the case. For example, spending on public cloud storage is predicted to reach 17% of total enterprise storage by 2017, up from 8% today. IT spending on cloud infrastructure, according to IDC, will exceed $37 billion in 2016, an increase of more than 15% from the previous year and, by 2020, nearly equal the amount spent on traditional, on-premises IT.
Phillip Marangella, CMO for EdgeConneX explores how edge data centers can help us rearchitect the internet in a way that will support the flood of data and massive traffic flows generated by emerging technologies like AI, cloud gaming, VR/AR and multi-cloud deployments, and more. The internet was not constructed to handle the traffic flows of today, and it's only going to get more congested in the coming months and years. Traditionally, traffic flows on the internet have largely been download-centric and networks have been built out to support those flows. However, the gravity of data and compute has shifted from the core to the edge as a result of technologies like the Internet of Everything (IoE), artificial intelligence and machine learning, cloud gaming and HD streaming and virtual reality. There is much more content and data that is now being created, stored and processed at the edge.
Over 72.5 million connected car units are estimated to be sold by 2023, enabling nearly 70% of all passenger vehicles to actively exchange data with external sources. The amount of data resulting from these smart vehicles will be overwhelming for traditional data processing solutions to gather and analyze, as well as the associated latency of processing this data-- leading to potential life-or-death scenarios, according to Ramya Ravichandar, from Foghorn. We speak with Ravichandar, about how connected car manufacturers are implementing edge AI solutions for real-time video recognition, multi-factor authentication, and other innovative capabilities to decrease network latency and optimize data gathering, analyzing and security. Digital Journal: What are the current trends with autonomous and connected cars? Ramya Ravichandar: Automotive companies are looking to improve real-time functionalities and accelerate autonomous operations of passenger vehicles.
Edge Computing (EC) allows data generated by the Internet of Things (IoT) to be processed near its source, rather than sending the data great distances, to data centers or a Cloud. More specifically, Edge Computing uses a network of micro-data stations to process or store the data locally, within a range of 100 square feet. Prior to Edge Computing, it was assumed all data would be sent to the Cloud using a large and stable pipeline between the edge/IoT device and the Cloud.