The IoT revolution is in full swing, bringing with it an army of devices (about 5.6 billion in three years) never before connected to the internet, including thermostats, motion detectors, and automobiles. With all the data that IoT devices generate, the need to process and analyze some of it closer to its creation point has become more important. That need has brought about a new model of cloud computing called Edge Computing. How does this new model change the way we think about data analytics and the cloud?
Let’s start with defining what the “edge” is. It’s a network location that is onsite or much closer (on the “edge”) to where the end devices live, and where the data they transmit is collected before being transferred to the (public) cloud. Edge devices can collect specific data from machines that are not IP ready (i.e., can’t connect to the cloud using Internet Protocol), store and process it, and then transfer it to the cloud for further analysis using IP-ready devices, such as field gateways.
Another name for edge computing is fog computing, a term coined by Cisco to describe the movement and analysis of data to the local network before it is sent to the cloud. Fog computing can also help connect smaller data centers to the public cloud.
How is edge computing used?
Edge computing can be applied across many different industries, but two popular examples of are in manufacturing and healthcare. Sensors on the factory floor or on the machines are transmitting tons of data about the health, safety and performance of said machines. That data all eventually gets uploaded into the cloud for analyzing, but sometimes companies need to make decisions based on that data before the cloud has finished processing it. Edge computing takes the most critical data, and stores and processes it in a location closer to the sensor source (the “edge” of the corporate network), before transferring it to the cloud for deeper analysis.
In healthcare, there are many IoT devices (pacers, insulin pumps, etc.) that constantly collect patient data. Edge computing using field gateways can filter and analyze the data before sending it to the cloud for further review. These gateway devices can also send alerts and detect certain health trends that may need less latency than if the data were simply transferred to the cloud.
Edge computing and Big Data
IoT devices generate a ton of data; hence, why it’s called “Big Data.” A key advantage of edge computing is its ability to filter out a lot of the “junk” that Big Data can generate to reduce the amount of bandwidth and storage needed to process that data in the cloud. Edge computing also notices patterns faster, allowing companies to react faster and remedy a potential emergency, such as a health scare.
It’s important to note that edge computing will not replace cloud computing, but merely act as a complement to it. Edge computing is designed to process mission-critical data or filter out the excess data subsets a company doesn’t need. There are still loads of data that need storing, analyzing and processing, and that will still be done in the cloud. Perhaps the two biggest drivers of edge computing are the ability of instantaneous response and the need to reduce the amount of data taking up storage and bandwidth space.
Edge computing is the next generation of cloud computing and Big Data analytics. As more IoT devices enter the market, prioritizing and organizing the ever-growing volume of data those devices product will be more important. As the IoT trend continues, it’s only more natural that edge computing will become more mainstream.
Our data centers are especially equipped to connect your edge devices to the public cloud to provide the ideal edge computing solution. Learn more about our data centers, or contact us to get started with a quote today.