AI/ML Driven Network Management: The Importance of True Observability

AI/ML Driven Network Management: The Importance of True Observability

By Matthew Vulpis

Digital transformation is disrupting enterprises in every industry by breaking down barriers between people, businesses, and things. This disruption comes mainly in the form of digital technologies, social, mobile, analytics, and cloud, which are impacting organizations and most areas of human activity. It is now a necessity for organizations to integrate these digital technologies and their capabilities to transform processes, engage talent, and drive new business models to compete and strive in the digital world.

Apart from the necessity of competition driving enterprises to upgrade technologically, the variety of benefits innovative devices and applications offer are also creating digital growth. The global digital transformation market size is valued at $594.5 billion this year and expected to reach $1.55 trillion by 2027. However, while these new technologies can add ease and optimization to daily processes, that isn’t to say the explosion of technology hasn’t come without challenges.

One of the most prominent hurdles among enterprises looking to expand digitally is the volume of data and network bandwidth requirements, which are growing relentlessly. With business processes happening in real-time and users expecting quick loading times, with feature-rich content, video streaming, and multimedia capabilities, most companies are generating seemingly unmanageable volumes of data and traffic on their networks today.

We caught up with Stephen Amstutz, head of strategy and innovation at Xalient, an IT consulting and managed services organization, specializing in software-defined networking security and security technologies, to discuss this explosion of data and how AI technology can help enterprises not only with network traffic but also help them do more than simply monitor their network.

“In this always-on environment, networks are completely overloaded, but organizations still need to deliver peak performance from their network to users with no degradation in service. But traffic volumes are growing, and this is bursting networks at peak hours, akin to the L.A. 405; no matter how many lanes are added to the freeway, there will always be congestion problems during the busiest periods,” said Amstutz. “This is a good example of where AI and ML can and is helping organizations take a proactive stance on capacity and analyze whether networks have breached certain thresholds. These technologies enable organizations to ‘learn’ seasonality and understand when there will be peak times, implementing dynamic thresholds based on the time of day, day of the week, etc., as a result. AI helps spot abnormal activity on the network, but now this traditional use of AI/ML is starting to advance from ‘monitoring’ to ‘observability.’

Artificial Intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. There are already a plethora of AI examples that consumers interact with on a daily basis, including chatbots and recommendation engines on streaming services.

The technology also has a variety of organization-level capabilities, and as of last year, edge AI had a market size of $590 million but is predicted to grow $1.84 billion by 2026.

The rapid growth comes as no surprise, as AI can offer a plethora of benefits across a variety of business aspects. In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states.

“Monitoring is more linear in approach. Monitoring informs organizations when thresholds or capacities are being hit, enabling them to determine whether networks need upgrading. Observability is more about the correlation of multiple aspects and context gathering and behavioral analysis,” said Amstutz.

“This delivers clear benefits to the business by reducing the time teams spend manually sifting through and analyzing realms of data and alerts. It leads to faster debugging, more uptime, better performing services, more time for innovation, and ultimately happier network engineers, end-users, and customers,” he continued. “Observability correlation of multiple activities enables applications to operate more efficiently and identify when a site’s operations are sub-optimal with this context delivered to the right engineer at the right time. This means a high volume of alerts is transformed into a small volume of actionable insights.”

“The network telemetry we are gathering, and that behavior analysis, means we are developing business insights, not just network insights. We can see if a gas pump stops creating traffic, which triggers a maintenance request to go and fix the pump. This isn’t a network problem, but the network traffic can be leveraged to look for the business problem. This is a use case for gas pumps and EV chargers but imagine how many other network-connected devices there are in factories or production facilities worldwide that could be used in a similar way.”

Overall, as society continues to push ever forward into a new digital age, the amount of data and bandwidth use, as well as network traffic, is only set to increase too. This means enterprises must find the solution to their network observability issues, lest they be left behind by the competition or fall behind on consumer expectations. For organizations of all sizes, industries, and verticals, the ability of AI is a promising answer to processing data more efficiently and accurately.

“Executives and boards want their network teams to be proactive. They won’t tolerate poor network performance and want any service degradation, however slight, to be swiftly resolved. This means that teams must act on anomalies, not thresholds, to understand behavior to predict and act ahead of time,” concluded Amstutz. “They need fast MTTD and MTTR because poor-performing networks and downtime impact brand reputation and ultimately cost money! This is where proactive AI/ML observability really comes into its own.”

Edited by Erik Linask
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