AI and network traffic analytics
Next-generation network analytics driven by artificial intelligence and machine learning promise to revolutionize conventional infrastructure management models, simplifying operations, reducing costs, and providing fresh insights. With machine learning and artificial intelligence solutions, the sheer amounts of data to analyze is an asset to be used rather than, as was once the case, a challenge to overcome.
The global network traffic monitoring software and traffic analysis tool market is projected to grow from $1.9 billion in 2019 to $3.2 billion by 2024. The growth is driven mostly by the increasing demand for sophisticated network monitoring tools and advanced network management systems that can handle the growing traffic and flow of information.
66% of the global population will be online by 2023. The increase in traffic is driven not only by users but also by the myriad of connected devices that form the IoT cloud. The share of Machine-to-Machine connections is estimated to grow from 33% in 2018 to 50% in 2023, while the consumer segment will rise to 74% of this share and business segment for 26%.
87% of organizations use network traffic analysis tools for threat detection and response, and 43% say NTA is a ‘first line of defense’ for detecting and responding to threats. Gartner defines NTA as “an emerging category of security product using network communications as the primary data source for threat detection and investigation within a network.”
Yet, as with many new technologies, AI-fueled analytics can only deliver its promised benefits if applied correctly to the proper problems. AI can be trained to pinpoint network failures and other shortcomings and bottlenecks, sometimes even before they happen. It can diagnose the cause of poor-quality network streams to find if the problem is in the service provider’s network, the backbone network or your ISP’s network.
It can also solve network congestion issues, provide bandwidth and delay estimation for better video or gaming experience, provide fair bandwidth allocation to users or within cloud data centers, fix insufficient network utilization and, in general, achieve a higher network performance and a happier customer.
A key component of network analytics tools is the dashboard used to interface with the team, which receives clear information regarding the network. The dashboard enables easier network performance monitoring and diagnostics and is a convenient way to convey technical knowledge to those who lack it.
The direct benefits of network traffic analysis include: avoiding bandwidth and server performance bottlenecks, uncovering apps that gobble up bandwidth, proactively reacting to a changing environment, managing devices exclusively and resource usage optimization.
Early AI-based network analytics tools were limited to collecting time-series data from the network and using anomaly detection – a specific class of ML techniques – to identify deviation from normal behavior. However, more advanced and specialized ML/AI algorithms can be leveraged to move from detection-only to detect-and-act, whereby the AI can take actions in real-time on the network as a result of the AI analysis.
AI can be a game changer. It can be trained to pinpoint the network problem using metrics easily collected across multiple points through the delivery path. By performing feature construction and data classification on those metrics, AI can detect problems with 80% accuracy, a figure that is bound to improve in the near future.
AI also enables the automation of certain detection, analysis and remediation actions, therefore enabling IT managers to shift their valuable time to less mundane tasks. AI-based automation in networks has the capacity to change the profile of network problems and their impact on organizations. Urgent network problems can be, in some cases, mitigated and completely resolved; in some other cases, converted into less-urgent support tickets.
Network operators must make sure they train their AI system with the correct type and amount of data, and with the right machine learning algorithm and approach for their specific application. While network IT staff need to master new skill sets in order to maintain and nurture AI-powered networks, trust between machine and man must also be developed as the new technology is entrusted with decision-making.
There may be some false positives during the early days of the AI use. However, if the IT manager is empowered in terms of how much control he/she can enforce over the AI actions, organizations can provide the value of automation without taking away the control that IT managers have over the network. There’s no doubt that AI, specifically machine learning, will disrupt the network operations and management business.
Glow’s role
Here at Glow, we help in the AI-based network traffic analytics space by getting the telecom networks ready to support 5G capacity needs, besides being an active player in field support, one of the most critical components of a network buildout.
