AI & machine learning for networks: example use cases

In today's digital age, the use of machine learning (ML) in networks has become increasingly prevalent. Modern businesses rely heavily on networks to maintain operations. However, it could be more and more challenging to manage network infrastructure effectively. One solution is to use machine learning (ML) algorithms to analyze network data and provide insights that can lead to more efficient network management. 

In this article, we will explore several ML use cases in network management including time series forecasting, capacity planning, intelligent alerting, and the use of external data to enable faster recovery of network components. Moreover, we will explain how these techniques can improve operational efficiency, reduce costs, and enhance overall performance. 

Network capacity planning 

Ensuring sufficient capacity is a key factor for proper operation of a networking or data center environment. However each physical or virtual device as well as the links between them have a determined maximal operational capacity. An important issue is not only the initial design of the network in terms of its capacity, but also the assessment of when the network capacity upgrade will be needed and which specific components it should include. One possible approach here is time series forecasting in relation to the volume of traffic that is observed in the network.

What is capacity planning in detail? 

Capacity planning is a critical aspect of network management, as it involves monitoring network resources and forecasting future capacity requirements. Machine learning algorithmscan help with automating the capacity planning process by analyzing historical network data and predicting future resource requirements. Thanks to that, companies are able to more proactively increase various types of both physical and virtual resources, while reducing unnecessary costs. 

Time series forecasting explained 

Time series forecasting is a technique that uses historical data to better predict future trends. Relevant ML algorithms analyze network data (e.g. traffic volume) to identify patterns and trends that enable accurate predictions of possible future network capacity needs. Summing up, time series forecasting allows for more efficient capacity planning and better infrastructure spending decisions.

Real-life capacity planning use case 

Below, you will find a link to a video, where a CodiLime’s solutions architect explains how AI/ML-based capacity planning works. Watch to learn how to implement more automation for your network management, better plan capacity upgrade operations, and reduce the stress level of the network operations team. 

Video - AI for Networks: Capacity Planning

Improved alarms 

In network management, alarms are used to indicate errors or failures. Very often one failure causes an explosion of alarms on other devices within a short period of time. The crucial problem then is to indicate the root cause quickly. Alerts are also raised when various network services’ performance indicators exceed predefined threshold levels. However, some alerts may be triggered unnecessarily when threshold levels are set inappropriately. The problem is how to filter non-meaningful alarms effectively. 

How can ML improve alarm handling in networks? 

Root cause detection

An explosion of alarms in the case of a failure is a tricky situation for network admins. Using available monitoring tools they need to quickly diagnose the situation, find the primary reason and fix the problems. It is a hard task, as the number of raised alarms can be large. ML techniques can be used to build patterns of alarms caused by specific failures. This is accomplished thanks to an analysis of a graph-relational database describing network environment components. Such patterns of alarms can then be used when a new explosion of alarms occurs to indicate the most probable failure causing it. 

Intelligent alerting

Alerts are essential for network management, but they can be overwhelming. ML techniques can be used to filter out non-meaningful alarms and to make alerting more intelligent. ML algorithms can analyze previous alarm data to adjust alarm thresholds and provide more meaningful alerts for the admins and network engineers in the future. Such algorithms can help trigger alarms only when necessary, providing better insight into network performance, improving network health, and reducing the burden on network administrators.

How to improve alarms - video explanation 

In the video, our solutions architect explains the ideas of applying different ML techniques to improve alarm handling. Graph ML techniques are suggested to prepare alert patterns that might facilitate detection of the root cause. ML classification algorithms are suggested for intelligent alerts filtration. By implementing them businesses can achieve faster root cause detection and increase automation for network management – for the details, click the video below. 

Video - AI for networks: Improved Alarms

Shortening fiber cut recovery time

In this case, external data is used for detection of high-risk situations or locations. In the event of a fiber cut, downtime can be costly for businesses. How can you shorten fiber cut recovery times? The solution is the use of external data, that can help reduce repair time and costs. 

Fiber cut recovery - what is all about? 

Fiber cut recovery is restoring a physical connection after it has been damaged. Fiber cuts can occur for various reasons, such as natural disasters, construction accidents, or intentional sabotage. 

Fiber cut recovery – use case video

Machine learning algorithms can be used to detect factors that may increase the risk of fiber cuts, such as excavators operating near fiber optic cables. ML algorithms can benefit companies with faster repair times, reduce downtime, and maintain operations. Watch the video to check how to achieve these benefits and learn more about possible external data sources: 

Video - AI for networks: Shortening fiber cut recovery time

Conclusion 

The use of machine learning in network management can lead to more efficient operations, reduced costs, and better infrastructure spending decisions. By applying ML methodologies and algorithms to network data, businesses can recover faster from various network failures. To explore these topics even more, we recommend watching the above-mentioned videos that provide a deeper dive into each use case.