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How AI, machine learning improve real-time communications traffic

Ready to move on from legacy quality-of-service policies for your real-time communications? AI and machine learning offer new ways to simplify and improve real-time traffic management.

Modern networks are causing a seismic shift in how real-time communications traverse IP networks to take the most optimal paths. Previous-generation techniques to manage traffic required static "if X, then Y" scenarios to be preprogrammed into networks on a hop-by-hop basis using legacy quality of service. But thanks to advancements in machine learning and AI, networks can take advantage of end-to-end network visibility and dynamic rerouting of data flows to dramatically improve real-time communications traffic performance and reliability.

Legacy networks rely on traditional quality of service (QoS) to help improve the reliability of real-time communication data flows, such as voice and video. QoS uses a three-step process of identification, marking and policy enforcement to give preferential treatment to critical flows, including real-time streaming applications.

Identification of data is required, so the system can know what packets should be favored over others. For years, this process was often a manual one, where a network administrator configured rules to identify IP packets based on factors such as source and destination IP address or protocol and port number. Network administrators also had to constantly edit rules when changes to the network or applications occurred.

Once communications traffic was properly identified, the network device would then be configured to mark data according to its importance compared to other packets coming in or going out of an interface. The network device would create policies based on those markings that would either allow traffic to process immediately, queue for delayed delivery or drop all together when interface congestion occurred.

While QoS indeed offered a way to improve the reliability of real-time communications, it was cumbersome to manage due to the manual setup methods, as well as the fact that every router or switch along a data flow path had to be managed separately. Thus, when AI and machine learning began to gain momentum in the network, one of the first use-case scenarios was to take on the challenge of simplifying and improving real-time communications traffic.

Three technologies are responsible for these improvements: Layer 7 application inspection, a centralized control plane and dynamic rerouting of individual data flows.

AI, machine learning create more intelligent networks for real-time communications

Advancements in AI and machine learning enabled network components to intelligently identify the purpose of a packet based on deep inspection down to the seventh layer of the Open Systems Interconnection model. Administrators are no longer required to manually create identification rules. Instead, the administrator simply chooses the flows based on what the network device has automatically identified.

The next technology greatly improving and simplifying the ability to prioritize real-time communications traffic on networks is a centralized control plane. With software defined network architectures, a centralized control plane architecture is the brains of the entire network and all of its components from one end to the other. Centralized control plane architecture is in opposition to a distributed, hop-by-hop architecture found in legacy network architectures.

The wonderful benefit of a single, centralized control pane is that QoS policy can be created once and simply pushed out to every network device on the LAN, significantly reducing the time to implement QoS and limiting the potential for misconfigurations along the data flow path.

Lastly, modern networks that use a centralized control plane also have the benefit of being able to see the entire data flow end to end. With this information, the network can identify areas of congestion and reroute high-priority traffic around problems. Rerouting data flows is a far more effective way to ensure data arrives in the most efficient way possible, compared with legacy QoS, which will either queue or drop packets at the expense of allowing high-priority packets to proceed.

Network administrators are only now beginning to tap into the potential AI holds as it relates to enhancing the transmission speed and reliability of data flows. While many concepts are still months and years away, one way to immediately take advantage of AI can be found with simplifying and improving the transport of real-time data.

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