Trends Shaping Higher Education: Wired and Wireless Networks

Trend Toward Machine Learning and Time-Sensitive Networking

Machine Learning and Time-Sensitive NetworkingI see two major trends that will affect the wired and wireless network through the next three to five years. First, machine learning is the ability for computers to learn without being explicitly programmed. It is a broad topic that is often linked with artificial intelligence and other advanced concepts. Within the wired and wireless network there are many components, such as access points and switches that not only move traffic from the edge of the network to other devices or to data center-centric or cloud-based applications but also capture information about the network equipment, the end points or the applications.

Machine learning applications capture all of the network information and look for trends. These trends are used for preventive maintenance by predicting component failure as the application monitors everything from packet loss across the network to equipment temperature and flagging equipment when historical models of failures are compared for reference. For campus administrators, machine learning means their technology investment lasts longer between necessary upgrades. It also means greater reliability of technology related to facilities operations and classroom learning. Additionally, machine-learning applications monitor thousands of pieces of information and dynamically change equipment parameters to provide or maintain better performance for students accessing resources across the network in the library or in the classroom.

And, while the above example focuses on networking, similar machine learning principles can be applied to other technology. For example, sensors in campus buildings can determine that there are no occupants and turn off the lights on a specific floor or adjust the temperature. These changes may not be time specific but situation specific based on multiple sensor inputs.

The second trend is on time-sensitive networking. Today, enterprise wired and wireless networks use protocols that are “best effort,” meaning that there is no specific guarantee when data will move from the device that sent it to its destination. This latency is best heard in the jitter that you might see on the computer monitor when streaming a lesson or the delay in a phone conversation on your smartphone. Historically, if we wanted a highly reliable, low-latency network, it required special, expensive and proprietary equipment. New advances in time-sensitive networking are seeing the ability for commercial equipment to address the deterministic requirements of applications. While, commercially, this means a better audio/video experience, it also has big quality-of-service implications for applications that to be prioritized, such as university research.

About the Author

Tim Zimmerman is research vice president at Gartner, Inc. in Stamford, CT.

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