Traditionally, network management tools focused on availability and performance. Because most applications were wired to the network, physical connections were monitored to assess how the network was processing information and to troubleshoot issues as they occurred. These tools were designed to manage infrastructure, not the user experience.
Today, network demands have changed how infrastructure works, thanks in large part to software-defined architectures, the Internet of Things (IoT) and the cloud. Data moves through a variety of traffic patterns to maximize utilization, reduce latency and control costs. In this type of network environment, you need a network management model that isn’t tied to hardware so you can maintain visibility into dynamic workloads.
The modern network needs equally modern analytics tools that focus on the user experience and aid in troubleshooting. Network analytics must be capable of analyzing and correlating data from a wide range of devices and applications. Most network analytics solutions, however, are only capable of analyzing one part of the user experience, such as connectivity or application performance. Then it’s up to humans to query and correlate data and determine what action needs to be taken.
Machine learning is being added to network analytics to not only troubleshoot issues, but to diagnose the root causes, and recommend corrective actions. A form of artificial intelligence, machine learning uses algorithms to enable technology to learn, identify patterns, make decisions, and perform tasks without being programmed to do so. The more data a machine learning program consumes, the more intelligent the program becomes.
In the case of network analytics, machine learning can sift through the noise from large volumes of data to deliver more powerful, precise insights. It can monitor traffic by the packet, discern patterns, pinpoint the cause of performance issues and recognize abnormal user activity. As a result, insider threats and hackers using stolen user credentials can be detected much more quickly. Machine learning can also provide greater visibility and control of IoT devices. Administrators automatically receive alerts about issues with individual devices, as well as recommendations for changes to optimize performance and security.
Aruba recently announced advances to its Mobile First Architecture, including NetInsight, an AI-powered network analytics and assurance solution designed to help you deliver the best possible user experience. NetInsight uses machine learning to automatically baseline network performance and connectivity patterns, monitor the network, recognize and provide insights into anomalies, and recommend how to optimize the network. These insights and recommendations are based on data that’s specific to user connectivity and radiofrequency performance attributes.
NetInsight also helps you improve Wi-Fi performance, even as traffic loads and patterns across mobile and IoT devices change. You’ll gain visibility into the user experience so you can proactively identify and resolve availability and performance issues before business operations are disrupted.
As organizations use more mobile and IoT devices and cloud environments, networks become more complex. The more complex the network is, the more difficult it becomes to meet user expectations. Let us show you how Aruba Mobile First Architecture and the NetInsight network analytics solution provide data-driven guidance that empowers you to optimize the user experience.