As the number of devices built for the Internet of Things (IoT) continues to grow, you’ve likely heard the term edge computing being used more frequently. With items like drones and advanced robots, these devices are increasing in their complexity. As a result, they require rapid processing of data. Can edge computing help enhance the speed at which data is transferred?
Edge Computing Defined
According to IDC, 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.” The most applicable use cases for edge computing lie in the IoT. Usually, items with sensors or embedded devices are the primary sources of data generation. Ask any research firm, and they’ll estimate there will be tens of billions of connected devices within the next few years – if we haven’t reached that number already. That’s a lot of data to transfer, and streaming to a centralized cloud or data center for processing might not be the most efficient. Edge computing, meanwhile, serves as an extension of those networks and the cloud.
Edge computing reduces latency and network congestion because data doesn’t need to travel over a network for processing. Particularly in industries where even a few milliseconds of latency could spell disaster, this is a huge advantage.
Edge Computing in Action
Things like a Fitbit or other wearable heart monitors are edge computing at its most basic. Users receive data on steps, heart rate, and sleeping habits on their monitors. The devices can provide and analyze that data without needing to connect to the cloud very often. More advanced use cases include gateways, such as a vehicle receiving and processing information from GPS devices, traffic signals, and even other vehicles to improve driver safety and efficiency. And perhaps the most complex use case yet deals with cell phones.
Chances are your current phone is on an LTE/4G cellular network. The buildout for the next generation of networks (5G) can take advantage of edge computing. As telecom providers build 5G into their wireless networks, they’ll add micro data centers either directly into or right next to 5G towers. Organizations that partner with telecom providers can then rent or own space in these micro data centers, using edge computing to gain direct access into the telecom provider’s broader network by connecting to a public IaaS cloud provider.
However, edge computing is not without its risks. As a rapidly evolving technology, security can be a concern with such a widespread network, especially if endpoints are unsecured. The cost of an edge computing project can also be prohibitive, particularly for smaller organizations.
How Will It Transform the Cloud?
Edge computing’s real power lies in nearly real-time insights. Per Gartner, about 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2022, Gartner predicts that number will increase to 50%. There are even some who argue edge computing could replace the cloud altogether. At Datapipe, we believe the two can work well in tandem.
What we may end up seeing is a hybrid model that combines the best of both worlds. Rather than an entirely decentralized architecture, cloud providers can deploy these micro data centers at a few key geographic locations. A provider can keep control while moving the data processing capabilities closer to the user.
As always, having the trusted guidance of a managed service provider can help in making these tough organizational decisions. As more devices enter the IoT and additional use cases develop, it’s going to be exciting to see what edge computing can accomplish.
Datapipe has joined forces with Rackspace to create the world’s leader in multi-cloud managed services. Learn more about the acquisition here.