Connected Intelligence: How Leveraging AI and ML Improves Network Ops

By Jed Rabe, Senior Wireless Engineer, CWNE#280 ACDX#925 —

Artificial Intelligence (AI) and Machine Learning (ML) have become transformative technologies across numerous industries, lines of business, and business disciplines. IT networking is no exception. While the term AI often conjures images of futuristic robots or advanced decision-making systems, its scope is far broader, encompassing algorithms and models designed to process vast amounts of data and make intelligent predictions.

In the realm of network operations, AI is rapidly evolving from a buzzword to a practical tool that simplifies and optimizes network management and monitoring. In essence, it provides us with connected intelligence.

As networks grow increasingly complex and IT staffing shortages are common, network engineers are being asked to do more with fewer resources. Traditional methods of network management are becoming less effective. AI and ML offer a new approach, enabling faster troubleshooting, enhanced visibility, and predictive maintenance, all of which lead to more efficient network operations. By automating routine tasks and providing actionable insights, AI empowers network engineers to focus on strategic initiatives rather than being bogged down by mundane operational issues.

How AI is Being Used to Manage and Monitor Networks

AI and ML are revolutionizing network management. The list below highlights five ways network vendors are adopting practical applications of AI:

1. Proactive Issue Detection: AI-driven systems monitor network traffic and identify patterns indicative of a potential failure. As an example, a switch port may be flapping and having excessive errors. An AI system could automatically perform a cable test and recommend replacing a cable based on the results.
2. Automated Configuration Management: ML models can analyze historical configuration changes and suggest or proactively make changes to the configuration. As an example, in wireless networking, the AI engine can analyze all the data from the radio frequency (RF) environment and automatically calculate an appropriate transmit power and channel assignment for the access points (APs) with greater accuracy compared to older radio resource management (RRM) models.
3. Enhanced Security: AI-powered tools can detect and respond to cybersecurity threats in real-time, identifying suspicious activity or unauthorized access attempts before they escalate.
4. Predictive Maintenance: By analyzing trends in device performance, AI-powered tools can identify failing hardware and proactively open support cases with the vendor before the hardware completely fails, resulting in downtime.
5. Traffic Optimization: ML algorithms can dynamically allocate bandwidth and optimize traffic, ensuring a seamless user experience — even during peak usage.

AI/ML Technology from Leading Vendors

Several networking vendors have embraced AI and ML to enhance their technology:

Juniper Networks: Mist

Juniper’s Mist was built from the ground up with AI/ML as its foundation. Marvis is Juniper’s Mist Virtual Network Assistant (VNA). Juniper’s Mist is the industry leader for integrating AI into their solution. Some key features include greater network insights, proactive identification and resolution of issues, enhanced network visibility, and quick support responses.

Use Case: Marvis has been used to identify missing VLANs from switch ports during wireless network implementations. This is a common issue where a switch port was misconfigured. The AP begins broadcasting its SSID and users can connect to the network but are unable to receive a DHCP address. Marvis can identify that a VLAN is missing on the switch port and notify administrators of the issue before users start opening support tickets.

HPE Aruba Networks: Aruba Central

HPE Aruba Networks has integrated AI/ML into their Aruba Central solution. Aruba uses a vast data lake for their AI to ingest information and make inferences that can be used to make recommendations to improve the performance and reliability of networks. Some key features include user experience insights, event-driven automation, and enhanced network visibility.

Use Case: Aruba’s AIOps can leverage Aruba’s vast data lake to compare your network with networks of similar sizes. The AI engine can quickly identify recommendations to improve performance or resolve issues before they become a problem. Keep in mind none of your data is being exposed. All data is anonymized and is secure.

Ruckus Networks: Melissa

Ruckus’s AI-driven analytics provide detailed visibility into network performance, enabling better troubleshooting and performance optimization. Melissa is Ruckus’s VNA. Some key features include automatic classification of service incidents, virtual network assistant for answering questions, and incident analytics.

Use Case: Ruckus’s AI engine can holistically review all active network issues and provide a prioritized list to help administrators troubleshoot/resolve the most critical issues first.

All the above AI implementations have similar functions, but each vendor does have key differentiators and are at different phases of development. No matter which vendor you use, AI/ML can help monitor and manage your networks.

Final Thoughts on Connected Intelligence with AI and ML

AI is a broad and powerful technology that is transforming network operations and providing administrators with much more connected intelligence. From proactively detecting issues to optimizing traffic and enhancing security, AI/ML tools are equipping network engineers with the means to effectively manage increasingly complex systems. Rather than replacing human expertise, AI complements it by automating repetitive tasks and providing valuable insights.

For network engineers, the rise of AI and ML is an opportunity to embrace innovation, streamline operations, and focus on strategic priorities. AI is becoming a trusted partner in building and maintaining smarter, more efficient networks. The future of networking is here, and it’s time to harness the power of AI to shape it.

Does your existing networking infrastructure allow you to take advantage of the tools and insight offered by AI and ML? If not, contact your Structured account manager or email [email protected] today! We offer professional IT services to help upgrade and optimize networks of all sizes. We also have deep expertise with nearly every networking vendor.

About the Author

Jed Rabe is a Certified Wireless Network Expert, a prestigious expert-level certification within the wireless networking professional community. In order to obtain this certification, Jed needed to first obtain the CWNA, CWSP, CWDP, and CWAP certifications; publish a number of articles; receive peer recognition and recommendation; and then be approved by the CWNE board. This certification recognizes that Jed is a wireless network professional that understands wireless administration, security, design, and analysis principles at an expert level.?