{"id":30227,"date":"2025-07-10T13:35:19","date_gmt":"2025-07-10T13:35:19","guid":{"rendered":"https:\/\/blog.lacnic.net\/?p=30227"},"modified":"2025-07-16T16:07:33","modified_gmt":"2025-07-16T16:07:33","slug":"ia-in-networks-operations","status":"publish","type":"post","link":"https:\/\/blog.lacnic.net\/en\/ia-in-networks-operations\/","title":{"rendered":"When Networks Become Too Complex for Humans"},"content":{"rendered":"\n

By Carlos Mart\u00ednez-Cagnazzo<\/a>, LACNIC’s Chief Technical Officer<\/em><\/p>\n\n\n\n

At LACNIC 43, we had the privilege of hearing from Christian Rothenberg<\/strong>, a professor at the University of Campinas and head of the INTRIG group, who gave an insightful talk on how artificial intelligence (AI) and machine learning (ML)<\/a> are reshaping network management.<\/p>\n\n\n\n

Christian took us through a journey of recent technological evolution, from network programmability to the emergence of AI models that enable the automation of tasks that go beyond human capabilities.<\/strong><\/p>\n\n\n\n

A key highlight of his presentation was Smartness<\/em>, a Brazilian research center funded by FAPESP for a period of 10 years (until 2032). Smartness acts as a bridge between academia and industry. With support from companies like Ericsson, its goal is to develop applied knowledge on networks, cloud computing, security, AI, and sustainability. This mixed approach is something he emphasized as essential: \u201cWe want our students to understand how the industry thinks, not just how to do research,\u201d<\/em> he noted.<\/p>\n\n\n\n

One of the most powerful ideas from the talk was that today\u2019s network is the first human-created system that can no longer be fully understood by humans themselves<\/strong>. From the variability of BGP to the mix of 4G, 5G, edge computing, submarine connectivity, network slices, and SDN, the complexity has reached a point where it becomes unmanageable without data-driven support tools<\/strong>.<\/p>\n\n\n\n

This is where Machine Learning<\/strong> comes in \u2014 not as a trend, but as a necessity. Christian<\/strong> showed how data-based techniques can detect anomalies, predict patterns, and suggest actions where traditional approaches fall short. Among the most mature or promising applications he mentioned are intrusion and anomaly detection, traffic classification by application type, traffic engineering and congestion prediction, BGP policy optimization, adaptive video streaming, XR and VR, and endpoint congestion control.<\/strong><\/p>\n\n\n\n

It is worth noting that many of these tasks are not new \u2014 what is new is the approach: learning from data instead of guessing with static rules.<\/strong><\/p>\n\n\n\n

What I found especially valuable was his practical suggestion for how to start applying AI in network operations. There is no single \u201ckiller app\u201d for AI in networks.<\/strong> \u201cInstead, there are many small operational pain points that can be addressed if we have well-collected data and a clear willingness to experiment,\u201d<\/em> he pointed out.<\/p>\n\n\n

Additional reading:<\/div><\/div><\/div>
What Can Artificial Intelligence Contribute to Network Operations?<\/a><\/div><\/div><\/section>\n\n\n

In the second part of his talk, Christian<\/strong> explained how artificial intelligence can be applied to networks. Without turning it into a theoretical lesson, he clearly described the three main families of Machine Learning techniques<\/strong> now used for network analysis and management. First, supervised techniques rely on data that has already been labeled. For example, we know certain records represent normal traffic while others correspond to suspicious events. Using these examples, the model \u201clearns\u201d to classify new, unseen events based on past patterns<\/strong>. This method works best when reliable, well-structured historical data is available \u2014 which, as he noted, is not always the case in the real world.<\/p>\n\n\n\n

Second, there are unsupervised techniques, where the model does not receive any labels \u2014 it is simply provided with data and expected to identify patterns, clusters, or similar behaviors on its own. <\/strong>This can help reveal users with unusual consumption patterns, anomalous traffic flows, or application segments that require special handling within the network. When network flows repeat with certain characteristics, they can be grouped (red, blue, green) and routed through different paths according to their profile. This is a way to optimize resources without the need for direct human intervention.<\/strong><\/p>\n\n\n\n

Finally, there is reinforcement learning, where the system learns through trial and error<\/strong>, receiving rewards or penalties based on the outcome of its decisions. Christian illustrated this with the classic example of a child touches a hot surface and does not do it again. In networking, this could involve an agent selecting traffic routes, evaluating results (congestion, latency, packet loss), and fine-tuning its strategy over time<\/strong>. What is interesting is that, when properly trained, this type of model can learn to \u201cplay\u201d with the network in an optimal way without having received explicit instructions.<\/p>\n\n\n\n

It All Starts with Data<\/strong><\/h2>\n\n\n\n

One idea that Christian emphasized repeatedly \u2014 and which I fully agree with \u2014 is that without good data, there can be no real intelligence<\/strong>. This point is especially relevant for our region. Many networks operate without a clear policy for observability and the storage of historical data<\/strong>, which limits their future ability to apply intelligent models.<\/p>\n\n\n\n

On the other hand, the techniques that Christian presented can be applied to a wide range of network scenarios<\/strong>, covering everything from reactive measures to predictive capabilities \u2014 including intrusion and anomaly detection, traffic classification by application or user type, QoE prediction for critical services like video, and real-time optimization of routing policies and resource allocation.<\/p>\n\n\n\n

It is remarkable that these tools are already accessible<\/strong> \u2014 many are open source and backed by strong academic and technical communities. Still, they demand not only reliable data but also close cooperation between business experts and technical specialists who can put these tools to work.<\/strong><\/p>\n\n\n\n

In the final part of his presentation, Christian made it very clear<\/strong> that machine learning models have significant limitations<\/strong>, especially when applied to networks. For instance, he pointed out that every network is unique <\/strong>\u2014 a true \u201csnowflake\u201d \u2014 which means that models are not always transferable from one environment to another. He also noted that if the data are imperfect<\/strong>, fragmented, or lack a standardized format, model performance will vary over time<\/strong> and require continuous retraining. Most importantly, the results must be interpretable and reliable<\/strong>, which is not always the case with complex models such as deep neural networks. \u201cIt is not enough for the model to work; we also need to understand why it made that decision,\u201d<\/em> he said, highlighting the importance of transparency in operational environments.<\/p>\n\n\n\n

The two final examples he shared were powerful because they connected theory and academic research with real-world impact.<\/strong><\/p>\n\n\n\n

First, he described a study in which his team was able to predict \u2014 up to seven seconds in advance \u2014 when a mobile connection would degrade and interrupt video playback. Network data, GPS location, and quality of experience (QoE) observations were enough to train a model that could now be valuable for operators or even streaming platforms.<\/p>\n\n\n\n

The second case, even more robust, showed how they managed to build a Network Digital Twin<\/strong> of an optical network to predict minor amplifier failures <\/strong>before they happened. This development was not only published and patented but has already been incorporated by an international network equipment manufacturer.<\/strong> It was the perfect example of every researcher\u2019s dream: taking innovation from the lab into the real world.<\/p>\n\n\n\n

His presentation made one thing very clear \u2014 and this strongly resonates with what we see in our region: artificial intelligence and machine learning are no longer futuristic ideas or passing trends<\/strong>; they are essential tools for managing networks that have grown so complex that, in many cases, they are simply beyond the reach of manual operations alone.<\/p>\n\n\n\n

There are no magic solutions <\/strong>\u2014 it is about recognizing that with the right data, genuine technical collaboration, and a clear focus on real business problems<\/strong>, it is possible to build solutions that are measurable, scalable, and truly useful.<\/p>\n\n\n\n

At LACNIC<\/a>, we see this every day: Latin America has what it takes<\/strong>. We have technical talent, active universities, committed network operators, , and a community that understands its context deeply. What we often lack is not talent but the bridges \u2014 the connections between academia and operations, between data and action, and between real-world problems and proof of concept.<\/strong><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

By Carlos Mart\u00ednez-Cagnazzo, LACNIC’s Chief Technical Officer At LACNIC 43, we had the privilege of hearing from Christian Rothenberg, a professor at the University of Campinas and head of the INTRIG group, who gave an insightful talk on how artificial intelligence (AI) and machine learning (ML) are reshaping network management. Christian took us through a […]<\/p>\n","protected":false},"author":6,"featured_media":30225,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[1554],"tags":[1557,1283],"archivo":[1345,1451],"taxonomy-authors":[1198],"tipo_autor":[],"class_list":["post-30227","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","tag-ai","tag-events","archivo-editions","archivo-highlights-2023","taxonomy-authors-carlos-martinez-en"],"acf":{"author":"","related_notes":""},"yoast_head":"\nAI in Networks Operations: Challenges and Real-life Applications<\/title>\n<meta name=\"description\" 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