Humans aren’t known for being patient. Technology has spoiled us even more. We want everything right now, whether it’s the answer to a question or access to a movie from a mobile device. It’s not that different for organizations across most industries. Fast, must-have-it-now access to data — and the insights it provides — is quickly becoming a requirement for normal business operations.
One of the impediments to that, however, is latency. Latency refers to the time it takes for a data packet to travel from its origin point to its destination. While the type of connection plays a role, latency is largely influenced by distance. No matter how fast a connection is, data has to physically travel between two points and that takes time.
Network complexity also makes a difference. Data doesn’t always travel along the same route because routers and switches prioritize and evaluate where to send the data packets they receive. If the shortest path between two points isn’t available, the data packets get routed through other connections. That can result in a greater distance and increased network latency.
Further complicating matters are Internet of Things (IoT) devices and technologies like artificial intelligence. They’re generating and processing massive amounts of data and using equally massive resources to do so. It takes time for all that data to travel from where it’s gathered to where it’s processed to where it’s ultimately used.
Data Centers at the Edge
One way businesses can leverage IoT devices, AI, and other resource-intensive technologies, all while minimizing latency, is to use edge data centers. While there’s no official definition of an edge data center, it’s basically a data center located close to the users it serves and/or data sources.
Edge data centers provide the compute, storage, and other resources for processing data as close as possible to end users. Because data doesn’t have to travel as far, latency issues are less likely to affect application performance and customer experience.
In our data center survey conducted January 2020, it was found that a substantial 91% of respondents are likely to select a data center provider that was located close to their users. It is probable these participants felt this way due to wanting the speed a closer data center could provide.
The use of “edge computing” via these data centers also offers the potential to help organizations reduce costs. The more data that can be processed locally, the less that needs to be processed in a centralized or cloud-based location where it’s easy for bandwidth charges to quickly add up.
Edge Data Center Use Cases
Edge data centers can provide resources to satisfy numerous use cases — particularly in market sectors where time is of the essence.
Medical IoT devices powered by an edge data center can access and process data quickly. That enables them to deliver rapid response times so medical professionals can provide more timely diagnosis and treatment. In an industry where a fraction of a second can mean the difference between life and death, the potential benefits are huge.
Autonomous vehicle providers require real-time application delivery to help ensure the safety of passengers, motorists, and pedestrians. Edge computing makes that happen.
Manufacturers can greatly improve operational efficiency and increase margins when they can leverage real-time analysis on the manufacturing floor. Edge computing enables this capability as well.
Retail organizations can use edge computing to optimize locally and in real time based on information processed at individual locations. It can also be used to enable real-time processing of supply chain systems without the costs and delays associated with data having to constantly travel back and forth to the cloud.
Many of the potential use cases aren’t restricted to any single industry. Case in point: any organization that wants to enable unprecedented levels of user engagement with augmented reality (AR) will appreciate the processing speeds edge computing delivers. It enables more vivid, faster, and improved AR experiences.
Those accelerated processing speeds also make it easier for companies to use resource- and data-intensive technologies such as machine learning.