Transforming Network Monitoring and Testing with Machine Learning







Transforming Network Monitoring and Testing with Machine Learning

Published: 2024/05/30

7 min read

One of the worst-case scenarios for any service provider is an unexpected system malfunction or equipment failure. It’s difficult to imagine a more stressful situation than a ticketing system suddenly becoming active. You can avoid this nightmare by using machine learning and predictive maintenance to conduct tests and perform network monitoring.

The market for machine learning (ML) is predicted to exceed $771.38 billion USD by 2032, growing at a CAGR of 35.09% from 2023 to 2032. An essential part of it will consist of ML-enhanced systems designed to detect anomalies, perform predictive analysis, and automate optimization tasks. Early detection of potential threats boosts network security and prevents failures for enhanced operational efficiency.

This article will discuss ML solutions in telecom software development that empower network monitoring and conducting tests. Let’s start with the latter.

Automated test platform – crucial challenges

The human factor is always a weak spot during testing. Even if a platform conducts tests according to a specified scenario, manual result verification limits its usability and can lead to several issues.

1. Lowering testing efficiency: Automatic tests are effective in executing specific scenarios, but the need for manual verification introduces limitations in assessing test results.,

2. Risk of human errors: Manual result verification increases the risk of human errors, leading to incorrect interpretation of test outcomes.

3. Slowdown of the testing process: Manual result verification adds an extra step, which significantly extends the time needed to conduct tests and deliver results.

4. Limited scaling possibilities: The absence of automatic verification complicates the scaling of the testing process, especially in large projects.

5. Rising operational costs: Manual result verification can escalate operational costs due to the need for additional human resources to review and assess outcomes.

6. Risk of overlooking defects: There is a high risk of ignoring potential defects, which can lead to the introduction of undetected errors into production.

7. Negative impact on performance: Delays associated with manual verification can adversely affect the development process’s overall performance.

How can machine learning boost testing?

ML is a branch of artificial intelligence that enables systems to learn from data, recognize patterns and make decisions or predictions without explicit instructions. By deploying algorithms that improve their performance over time as they are exposed to more data, ML enhances automation and decision-making capabilities across various domains. Since ML enables testers to achieve comprehensive testing with less manual effort, focus on strategic tasks and identify issues that traditional methods might miss, it effectively addresses the needs of the telco sector.

Employing ML in telco tests

Implementing an ML solution using supervised learning and AutoML will automate the process of identifying the root cause of problems, thus improving testing efficiency and accuracy. How does this exactly work?

Supervised learning utilizes labeled historical test data to train the ML model in recognizing specific problem types. The ML model learns from past test scenarios and outcomes, enabling accurate classification of future test results.

AutoML streamlines the development of ML models, making sophisticated ML accessible without deep technical expertise. Accelerating the model creation process allows for rapid deployment and iteration.

Benefits of using an ML model in telco tests

To sum up, there are several benefits associated with integrating machine learning solutions into telco testing.

1. Increased testing efficiency: Execute specific scenarios while an ML model increases the efficiency of result validation.

2. Reduced human error: Validate tests and results by using an ML algorithm – independent of human intervention.

3. Improved the testing process: Conduct more tests with quick validation of results.

4. Less mean time to repair (MTTR): Improve problem detection and boost MTTR and customer satisfaction levels.

5. Lower operational costs: Save resources by using fewer staff members to perform more tests.

6. Introducing AIOps: Automate IT operations by leveraging ML and analytics with a dedicated artificial intelligence for IT operations (AIOps) team.

7. Enhance performance and minimize delays: Reduced verification time improves the performance of the development.

8. Automating root cause analysis: Streamline the process of finding the root cause of issues while conducting analysis.

Network monitoring using ML-enhanced solutions

Machine learning is a powerful tool that enables engineers to take preemptive actions before critical incidents occur. By leveraging the correct set of preselected data, an ML model can accurately detect anomalies and patterns that could indicate potential threats or issues before they manifest.   

How does an ML monitoring model work in telco scenarios? 

Data is collected, processed and becomes part of an ML pipeline that extracts and selects relevant features from network data. By using historical data, a ML model is trained for anomaly detection, predictive analysis, supervised learning, unsupervised learning, or a combination of them. Decision engines can identify anomalies or potential issues with incoming data in real-time. Alerting and reporting mechanisms generate warnings for anomalies or predicted issues, and dashboards and reporting tools provide interactive reports for network performance and incident tracking.

What are some scenarios where ML-based monitoring might prove useful?

Total disaster scenario

In the case of a power supply failure or a device malfunction in the server room or other network component, the operation of many services may be affected. Consequently, several connected systems and sensors will report problems, resulting in a flood of alarms of varying priority. In such cases, it is essential to identify and highlight the root cause of the multitude of events occurring.

Calm before the storm scenario

When a particular part, e.g., a Small Form-factor Pluggable (SFP) network module or another device, starts to fail (but has not completely failed yet), it is typical for the monitoring system to send an alarm and then remove it after a while (e.g., a few seconds) because the problem goes away. Depending on the individual system, it may be easier or harder to notice such a situation.

Key features of a ML-enhanced system

  • Anomaly detection: Deploy ML algorithms to detect unusual network activity that indicates potential security threats or system malfunction.
  • Predictive analysis: Implement predictive models to foresee and prevent network failures, thereby enhancing overall network stability.
  • Automated optimization: Utilize continuous learning algorithms for ongoing network performance optimization and efficient resource management.

Critical components of network monitoring with ML

  • Enhanced network security: Detect security threats quickly through sophisticated pattern recognition.
  • Increased operational efficiency: Minimize downtime and improve network reliability through predictive maintenance.
  • Strategic insights: Leverage network data to provide valuable, actionable insights for decision-making.

Predictive maintenance models in telco solutions

Predictive maintenance leverages data analysis and ML to anticipate and prevent equipment failures before they occur. Organizations can avoid issues by monitoring networks and solutions in real-time and scheduling maintenance activities to reduce costs and maximize asset lifespan.

Here’s a brief overview of what predictive maintenance entails.

1. Data collection: The first step in predictive maintenance is collecting data from various sources. In the context of network equipment, this could include performance metrics, operational logs, error messages and physical sensor data. The data gathered provides a comprehensive view of the equipment’s health and performance over time.

2. Data analysis and pattern recognition: The collected data is then analyzed to identify patterns and trends. ML algorithms are trained on historical data to recognize signs that indicate potential issues or impending failures. For example, a sudden increase in temperature or unusual network traffic patterns might precede a hardware failure.

3. Predictive modeling: Predictive models are developed after recognizing patterns. These models can forecast future equipment failures by detecting anomalies or deviations from normal operating conditions, becoming more accurate as they process more data over time and learn from every new piece of information.

4. Proactive intervention: Based on the insights gained from existing models, maintenance can be scheduled proactively before a predicted failure occurs. Such an approach allows for interventions to be planned during non-critical times, thus minimizing operational disruptions and avoiding catastrophic failures.

5. Continuous improvement: Predictive maintenance is an iterative process. As more data is collected and analyzed, ML models are continuously refined and improved, leading to more accurate predictions and efficient maintenance schedules.

GenerativeAI implementation will empower network monitoring

According to Gartner, more than 30% of the increase in demand for application programming interfaces (APIs) by 2026 will come from AI and tools using large language models (LLMs). Generative AI and ML-based tech can bring cutting-edge solutions to enhance network monitoring.

An effective way to monitor a system is by integrating it with a company’s preferred messenger. Team members can use a regular chat window to input queries about the system’s status. An AI-based solution can alert the admin if there is a potential malfunction by sending them a message, just like a colleague would do.

The future of AI-powered network monitoring

By incorporating machine learning-based predictive modeling into their workflows, telecommunication engineers can enjoy faster, more efficient and more dependable operations, while managers responsible for operations can expect increased productivity, improved safety and reduced costs. Software Mind specialists combine telco expertise with smart AI and ML implementations to design custom solutions for telecom companies – which is why Software Mind is a trusted partner for telecommunications operators around the world.

If you want to integrate AI observability and machine learning into network monitoring, improve your operational efficiency and deliver a better service for your customers, use this form to contact one of our experts.

About the authorMateusz Żelazko

Principal Software Engineer

A Principal Software Engineer, Mateusz has over 10 years’ experience in designing and implementing Java and microservice-based systems for businesses in the telecommunications and manufacturing industries. As an active contributor to the Security Guild, his professional interests revolve around the security of web applications, system optimizations and finding solutions to performance issues.

About the authorPiotr Brzózka

Sales Director

With over 15 years' sales experience in telecommunications and software development, Piotr is the bridge between the technical expertise and business knowledge that companies need. As Sales Director at Software Mind, Piotr has been crucial in building dedicated developing teams for start-ups and scale-ups across Central Europe. Passionate about helping organizations across sectors achieve digital accelerations, Piotr is committed to delivering autonomous teams who take ownership, match an organization's culture and engineer forward-thinking solutions.

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