Edge Computing vs. Cloud Computing: What’s the Difference?
There’s a common misconception that cloud and edge computing are synonymous because many cloud providers — such as Dell, Amazon Web Services and Google — also offer edge-based services. For example, an edge cloud architecture can decentralize processing power to a network’s edge.
But there are key differences between cloud and edge computing. “You can use cloud for some of the edge computing journey,” Gallego says. “But can you put edge computing in the cloud? Not really. If you put it back in the cloud, it’s not closer to the data.”
Gallego notes that while cloud services have been around for more than a decade, edge computing is still considered an emerging technology. As a result, colleges and universities often lack the in-house skills and capabilities to make use of this technology. If that’s the case, an institution may want to work with a partner to help it get started.
What Is Edge Computing Used for in Higher Ed?
The most common use case for edge computing is supporting IoT capabilities. By bringing servers closer to connected sensors and devices, institutions can leverage Big Data to gain actionable insights more quickly.
By placing clouds in edge environments, institutions can also cut costs by reducing the distance that data must travel. For an increasingly connected campus, edge computing can also help reduce bandwidth requirements.
As campuses prepare to support the next generation of students (the children of millennials), edge computing will play a key role in bolstering campus networks. Sometimes called “Generation AI,” this cohort will be using AI technologies in almost every aspect of their lives. To support an exponential amount of AI-enabled IoT technologies connecting to campus networks, universities and colleges will need 5G networks and mobile edge computing.
What Are Some Examples of Edge Computing?
Edge solutions make it possible for post-secondary campuses to adopt what Gallego describes as a three-tiered computing model: on-premises, at the edge and in the cloud, with each fulfilling a specific purpose.
Onsite servers might be used to securely store confidential financial or research data, while the cloud underpins hybrid and remote learning frameworks. Edge computing, meanwhile, offers benefits for data-driven research, especially time-sensitive research projects that require immediate data processing.