Anticipating capacity requirements
Getting a solid handle on future network capacity needs is a relatively simple predictive analytics problem, says Steven Toy, senior director of information technology at SAS. "Figure out the metric you’re interested in measuring against, capture the network capacity data, then compare," he says. Say that an organization, for example, wants to upgrade its circuits when they're reaching 75 percent of capacity. "Gather data for several months and forecast where you’ll be in three to four months — roughly the time it takes to provision new circuits," Toy says. "When your analysis shows you’ll be at 75 percent in three to four months, start the procurement process."
Predictive algorithms can be applied against traffic, service, device and user behavior, essentially extending standard network statistical planning activities to cover many more dimensions of network and technology performance, Noya says. "Presently, the capacity planning approach relies on reference performance KPIs that are certified by technology providers and supported by the engineering design," he notes. "Application of AI /machine learning algorithms enables improvement of this approach with a continuous learning process to improve performance beyond what would be possible using static KPIs."
Ensuring performance and quality
"In the case of network performance and quality issues, forecasting algorithms help manage multiple dimensions of the analysis and decide which events have the most impact on results," Noya says. Deep learning can be a particularly useful tool for network performance/quality optimization. "When you have a dataset that includes records of events you want to predict, you can train a deep neural network on that data," Nicholson says. If the deep net is properly trained, it can accurately predict when those events are likely to occur. "When you can predict capacity problems accurately (for example), you can act pre-emptively to rebalance the load on your network and provision the network with more capacity," he explains.
According to Toy, network performance and quality problems are similar to manufacturing problems. "The more data you have about the manufacturing process, and the more information you have about problems coming into repair centers, the more easily you can predict failure," he notes. It’s pretty much the same for network problems, he says. "Predict failures and performance problems at the edge and in the core of your network by forecasting error rates, predict the failure of components based on logs, and take action before the problem is noticeable."
Predictive analytics can also examine trends in data traffic patterns based on usage type and provide an early warning whenever it discovers possible issues. "For example, low priority real-time traffic that uses UDP (User Datagram Protocol) will start seeing performance issues before higher priority traffic is impacted," says Atif Mir, CIO network infrastructure advisory leader at professional services firm KPMG. "A good and empowered predictive analytics tool can predict the impact and, if empowered, can make changes to avoid such impact," he explains.
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