Using predictive analytics to troubleshoot network issues: Fact or fiction?

Predictive analytics can reveal lurking network problems before they impact reliability or performance. Once considered a futuristic technology, predictive analytics is poised to become a mainstream network diagnostic and management tool.

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Predicting the future is getting easier. While it's still not possible to accurately forecast tomorrow's winning lottery number, the ability to anticipate various types of damaging network issues — and nip them in the bud — is now available to any network manager.

Predictive analytic tools draw their power from a variety of different technologies and methodologies, including big data, data mining and statistical modeling. A predictive analytics tool can be trained, for instance, to use pattern recognition — the automated recognition of patterns and regularities in data — to identify issues before they become significant problems or result in partial or total network failures.

"Relying on multiple sources of clean data, along with built-in redundancies to deliver good, accurate information, visibility in the network can prevent issues rather than simply reacting to them," says Richard Piasentin, chief strategy officer at network performance specialist Accedian. He notes that analytics can even be integrated into closed-loop orchestration systems to provide network self-correction for many common problems. "Ultimately, predictive analytics ... helps companies save on operational costs and prevents issues from going unnoticed — issues that usually culminate in complete outages," he says.

Analyzing network behavior, infrastructure thresholds

When properly designed and deployed, predictive analytics can deliver deep insights into an array of commonplace and unique network issues, helping operators handle everything from policy setting and network control to security, says Rahim Rasool, an associate data scientist with Data Science Dojo, a data science training organization. To tackle security issues, for instance, predictive analytics can use anomaly detection algorithms to sniff out suspicious activities and identify possible data breaches. "These algorithms scan the behavior of networks working in the transfer of data and distinguish legitimate activity from others," Rasool explains. "With predictive analytics systems, the vulnerabilities in a network can be detected before a hacker group does and, subsequently, a defense mechanism can be drawn out."

Another way predictive analytics can help organizations is by comparing trends to infrastructure capabilities and alert thresholds. "Almost all signals have an upper bound and a lower bound that are a result of the infrastructure's capabilities," says Gadi Oren, vice president of technology evangelism at LogicMonitor, which operates a cloud-based performance monitoring platform. "For example, a certain device interface can only transfer so much capacity per unit of time before it is saturated," he says. Additionally, some signals are linked to alert thresholds. "Using the exposed trend and its variance, we can predict when a certain physical system will max its capacity or when the trend is expected to reach a threshold and cause an alert."

Predictive analytics at work

Although just about any type of enterprise network can benefit from predictive analytics-generated insights, networks transporting crucial data in life-or-death sectors such as health care, emergency response and aviation traffic management stand to gain the most from the technology.

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