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Improving Network Analysis and Troubleshooting with Machine Learning

Unpacking the conversation from Shark Bytes Podcast: Unveiling the future of automation, AI, and network analysis and troubleshooting.

In July 2023, B-Yond’s Vice President of Product Management and Growth, Johnny Ghibril, one of the leading figures at B-Yond, was invited to be one of the guest speakers in the Shark Bytes podcast at Sharkfest, hosted by Roland Knall. Anand Ravi, the company’s Chief Product Architect joined him as well.

In this podcast, the trio discussed issues related to network analysis and machine learning, the two major components that make up AGILITY, B-Yond’s premier product. In this article, we’ll recap and expand upon the topics that were discussed in the podcast, which included AI and its role in network analysis and troubleshooting.

Table showing the role of machine learning in network analysis and troubleshooting

Introduction to the Role of Machine Learning in Network Troubleshooting

The rollout of 5G and ever-changing network topologies has amplified the need for advanced tools and methodologies in the telecommunications industry. Within this context, the role of machine learning in network analysis and troubleshooting has become paramount.

Detecting False Positives with Machine Learning

One of the standout capabilities of machine learning in this domain is its ability to detect and respond to false positives. These are scenarios where, from a customer's perspective, everything might seem operational, but there are underlying variations in the entire network that could potentially escalate into significant issues. By harnessing the power of machine learning, companies can get deeper insights into these intermittent variations, enabling them to address potential problems before they impact the end-user.

The Shift from Traditional Troubleshooting

Historically, many of the complex cases telecommunications engineers encountered were nearly impossible to solve without a strong indication of the root cause. With traditional methods, they had to rely heavily on their knowledge and expertise, and ensuring trust in their judgment was always a challenge. However, with the advent of machine learning tools, this paradigm is shifting. Engineers can now offload a significant portion of the time-consuming troubleshooting tasks to these automated systems, allowing them to focus on the more intricate 10% of cases.

Transformation in the Telecommunications Sector

The telecommunications sector, particularly wireless, is undergoing a transformation. The push to introduce performance-sensitive applications for mobility and to move enterprises onto cellular services necessitates a higher quality of service. Given the sheer scale and complexity of modern networks, it's impractical to rely solely on human engineers to manage and troubleshoot them.

Scalability and Efficiency with Machine Learning

This is where machine learning steps in, offering scalability and efficiency. By automating a significant portion of the analysis and troubleshooting tasks, machine learning ensures that the limited number of expert engineers can focus on solving the most critical issues, thereby optimizing network performance and enhancing user experience.

Exploring the Challenges with Automation and AI Technologies

Image of AI Neurons

Specificity and Variability of Network Topologies

There are multifaceted challenges that come with the integration of automation and AI technologies, particularly in the realm of telecommunications, network management and network troubleshooting. One of the primary challenges highlighted is the specificity and variability of network topologies across different customers.

As technologies like 5G are rolled out, entire network topologies are not only constantly changing but can also be highly specific to individual customers. While some patterns may be generalized across various clients, others require specific, tailored solutions. This variability poses a significant challenge for automation and AI systems, which must be adaptable and flexible enough to cater to these unique requirements.

Human Intervention and the Issue of False Positives

While automation can address a substantial portion of common issues, it's the unique, hard-to-diagnose problems that often require human intervention. Historically, engineers had to rely heavily on their expertise and judgment, facing challenges in ensuring trust in their decisions. Automation and AI technologies can sometimes provide strong indications or insights, but they may not always pinpoint the exact nature of the network issues.

This can lead to situations where the technology might indicate a potential problem, but the onus remains on human experts to delve deeper and ascertain the root cause. Additionally, false positives also present a significant challenge. From the perspective of machine learning, false positives can arise when the system incorrectly identifies a network issue, therein the problem lies. Addressing these false positives is crucial to prevent unnecessary interventions and to ensure that resources are directed towards genuine issues.

The Limitations of Large Language Models (LLMs)

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While large language models like ChatGPT have revolutionized the way we perceive AI, they come with their set of challenges. One of the primary concerns is their potential to generate what experts term as "AI hallucinations." These instances, where the AI deviates from accuracy, can be a hurdle, especially when precision is paramount.

For instance, when Johnny conducted an experiment to generate sample PCAPs, what looked like very convincing results produced by ChatGPT were discovered to be inaccurate when they were closely examined by Anand. So for now, B-Yond has focused their attention more on open instruction large language models. These are platforms that allow users to employ large language neural networks and generative AI to train them on specific data sets. This approach offers much more flexibility, wherein users can provide the data set for the large language model and utilize these generative AI methodologies to create something more tailored to their specific needs.

Distinguishing between traditional machine learning models and large language models is also a challenge. While both have their merits, understanding their capabilities and constraints is crucial for optimal utilization. Experimentation is the key here. By continually testing and iterating, companies can find the sweet spot where these models can be most effective.

Improving Accuracy: Tailoring Models through User Reinforcement

Improving the accuracy of models, especially in the intricate realm of network analysis, requires a holistic approach that integrates user feedback and continuous reinforcement. There is a constant need to tailor models to cater to specific customer needs and nuances. With advancements like 5G, the need for models that can adapt to unique customer patterns is indispensable. Some of these patterns can be generalized across different clients, but others necessitate specific, bespoke solutions.

The Role of Knowledge Transfer in Model Refinement

One of the primary strategies is the importance of knowledge transfer. While certain services, such as Voiceover LTE or 5G connectivity, may have well-defined standards with minor variations across customers, the key lies in constantly mapping these standards and then identifying deviations within individual customers. By continuously updating and refining the models based on real-world network data and feedback, the system can better recognize and adapt to changing patterns.

User Reinforcement and Feedback for Enhanced Accuracy

Additionally, user reinforcement plays a pivotal role in enhancing model accuracy. Once models are deployed at a customer's site, initial accuracy levels might hover around 80%. However, through a process of feedback and reinforcement, where engineers and experts provide insights on what the model got right or wrong, the accuracy can be enhanced considerably.

After a few months of such iterative feedback, models can achieve accuracy levels well above 90%. This feedback loop, where users actively participate in refining and reinforcing the model, ensures that the system remains robust, adaptable, and aligned with the ground realities of the existing network.

A Multi-Pronged Approach to Model Accuracy

Improving the accuracy of models in network analysis necessitates a multi-pronged approach that emphasizes user feedback, continuous reinforcement, and the ability to tailor solutions to specific customer needs. By integrating these strategies, network models can achieve higher levels of precision, ensuring more effective network management and troubleshooting.

Scaling Machine Learning for Efficient Network Problem Resolution

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The integration of machine learning into complex network analysis has ushered in an era of efficient problem resolution, a vital necessity given the evolving complexity of modern network systems. As the introduction of technologies like 5G continues to unfold, networks are becoming increasingly intricate, necessitating advanced tools for efficient troubleshooting. Machine learning offers the capability to handle these complexities by recognizing broad patterns and providing insights into network behavior that might be challenging to detect with traditional methods.

Customizing Machine Learning for Individual Network Behaviors

One of the primary advantages of scaling machine learning in this domain is the ability to tailor the learning models based on specific customer needs and network behaviors. While there exist standard services, such as Voiceover LTE or 5G connectivity, that adhere to global standards, there are also nuances that vary from one customer to another. Machine learning models can be trained to understand these nuances, ensuring that the system is not only recognizing the standards but also identifying deviations specific to individual customer networks. By doing so, the models can offer solutions that are more aligned with individual customer requirements.

Continuous Refinement for Model Accuracy and Efficiency

As machine learning models are exposed to more data and receive continuous feedback, their accuracy improves significantly. For instance, once a machine learning model tailored for 5G is deployed at a customer's site, it can achieve about 80% accuracy right from the outset. With continuous feedback and reinforcement from engineers, the model's accuracy can further increase to well above 90% within a few months. This iterative process of feedback and refinement ensures that the model remains relevant and efficient in solving the challenges it encounters.

The Transformation in Cellular Service Through Machine Learning


As technologies evolve and the rollout of 5G becomes more prevalent, the nature of cellular networks is changing. Historically, cellular service, especially in certain regions, has been perceived as a "best effort" solution. It was an accepted norm for calls to drop occasionally or for connections to be lost momentarily. However, with advancements in technology and increased user expectations, there's a discernible shift in this perception.

Machine learning aids in understanding and addressing these expectations. It offers insights into false positives and can detect intermittent variations in the network, which might be invisible to the naked eye but could affect the user experience. By analyzing packet captures and providing visibility into network flows, machine learning tools offer a deeper understanding of the network's health and performance.

There is currently a significant push to move enterprise operations onto cellular service. The expectations associated with these services, especially from businesses, are considerably high. With significant investments in infrastructure, such as increased densification and more towers, the focus is on ensuring consistent, high-quality service.

Drones, Industry 4.0 applications, and other performance-sensitive applications are expected to operate flawlessly on these complex networks. To achieve this level of quality, especially at such a vast scale, traditional methods fall short. This is where machine learning steps in, offering the ability to analyze vast amounts of network data quickly and efficiently, ensuring that network performance is optimized.

Conclusion: The Future of Network analysis and troubleshooting

The future of network analysis and troubleshooting is on the verge of a significant transformation, driven primarily by the integration of machine learning and AI technologies. As the rollout of advanced networking technologies like 5G continues, the intricacies and demands of modern networks amplify. In such a scenario, traditional methods of network analysis may not suffice. Machine learning offers the capability to delve deeper into network flows and provide insights that were previously challenging to extract.

One crucial benefit of incorporating machine learning into network analysis is the ability to detect and understand false positives and intermittent variations in the network. These anomalies might seem insignificant but can have a profound impact on user experience. Furthermore, the capability of machine learning to analyze vast amounts of network data and recognize patterns offers a more proactive approach to troubleshooting, predicting potential issues before they escalate.

As enterprises increasingly move their operations onto cellular networks, the expectations for consistent, high-quality service surge. This necessitates a shift in the way complex networks are analyzed and troubleshot. The focus will be on ensuring network performance that aligns with the rising expectations of users and businesses. By integrating machine learning, it becomes feasible to analyze vast datasets, optimize network performance, solve network issues, and ensure a level of service quality that was previously challenging to achieve.

In essence, the future of network analysis and troubleshooting will be characterized by a blend of machine learning technologies and human expertise. While machine learning will offer the tools and insights necessary to understand and optimize networks better, human expertise will remain invaluable for interpreting these insights and making strategic decisions. Thus, as networks evolve and become more complex, the synergy between machine learning and human expertise will be pivotal in ensuring optimal network performance and user satisfaction.

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