The traditional WAN model has seen dramatic changes over the past few years. Pre-pandemic, it was widely used to connect employees working in offices to their information and applications, distributed across a few company data centers of varying size, type and sophistication.
Today, the expectation is that any employee should be able to work from any location, at any time, without any restrictions or degradation of service. Today’s modern IT and networking environments need to support this cloud-first world and everything that follows. WANs need to evolve to support the modern enterprise – artificial intelligence (AI) can help.
Dealing with Complexity
Visibility and control over network traffic and services has been one of the hallmarks of WANs. Organizations need that insight and control in order to better direct critical traffic and ensure it gets where it needs to, when it needs to. Optimizing network traffic and performance helps to protect the end-user experience and ensure satisfaction.
It goes without saying that modern networks are increasingly complex. The number of devices and connections has increased exponentially, meaning that network management needs to improve alongside it.
Because the rate of expansion is higher than a typical network team can handle, AI must be introduced to keep the already complex network structure of the present manageable, while enabling organizations to be ready to manage expansions in real-time.
Boosting Traffic Routing
At Juniper Networks, we understand that modern networking success must be judged on the experience delivered to end users. Are information and applications available immediately, without delay? Do video calls have lag or experience drops? Does the network enable employees to work efficiently and effectively, or does it stop them from doing so?
This is where AI can be a huge benefit – ensuring that the proper traffic is prioritized and routed most efficiently, to enhance (and protect) a positive end-user experience. AI can be integrated to prioritize certain types of traffic, to watch for degradation or other signs of trouble and to either alert the network team or make adjustments itself. As the AI understands all of the variables involved better and better over time, it can even take predictive actions, understanding the cause and effect of typical situations and making adjustments ahead of time.
Understanding the network, the service level requirements, user needs and the best route for traffic – and optimizing for all of them at the same time is not a simple task. By applying AI to the problem, it becomes a much more manageable one, freeing up the network team’s time to act on more critical matters.
IT and networking teams can quickly become bogged down in the details of operating their organizations’ networks: choosing traffic paths, reviewing security alerts, troubleshooting problem areas and more. With an increasing amount being asked of networking teams, they need their networks to help make their jobs easier. The application of AI can not only help them to manage ever-growing, complex networks, but it can also speed the setup and repair aspects of their jobs as well.
AI can assist in the provisioning phase, helping network teams optimize rollouts of equipment while also alerting on anomalous attempts to connect new sites or services to the system. AI can isolate, alert and prevent these attempts from being successful and can adjust and roll out new policy definitions based on newly added equipment or functionality.
The ability to quickly diagnose and repair issues is yet another reason why teams should apply AI to the network. When problems arise or alerts sound, the networking team jumps into action to identify whether the alert is a major problem that needs to be addressed right away. Then, the root cause of a problem needs to be searched out, isolated and repaired. The challenge, however, is that as a company’s networks continue to grow and expand, so do the problems – and the alerts. It’s rare that an internal networking team grows in size to match an expanding company network, which can mean there’s an impossible amount of alerts to investigate and manage.
AI can review and rank alerts as to which are critical, which can wait and which are false positives. Well-trained AI can also make adjustments and repairs on its own, such as increasing bandwidth of the network. One of the more powerful applications of AI in the operations process is to speed the time to repair of any issues. Historically, alerts give network teams an idea of what the problems could be, but that’s all. AI can guide teams to exactly what the issue is – i.e., why there is lag, why video calls are being dropped – and give direction on the repair steps to take.
Simplifying roll-out and management can speed the time-to-value and time-to-repair, removing time-consuming, repetitive tasks from network teams.
Juniper SD-WAN driven by Mist AI
Juniper’s AI-driven SD-WAN technologies make these applications of AI possible in today’s modern networks and improves both end-user and IT user experiences. For more information on how AI can help organizations move faster and more effectively, watch the on-demand webinar: SD-WAN Redefined Webinar Series Part 1: The Case for AI in SD-WAN with John Burk, Nemertes Research.