Hawkeye Leverages Machine Learning to Identify and Troubleshoot Problems to Help Keysight Enterprises Shorten Network Outages and Improve Uptime
Keysight Technologies, Inc., a leading technology company that helps enterprises, service providers and governments accelerate innovation to connect and secure the world, announced the addition of new machine learning (ML) features in the Hawkeye active network monitoring platform from Ixia, a Keysight business. The addition of machine learning enables Hawkeye to help enterprises shorten outages and improve network uptime by quickly detecting, identifying and resolving network anomalies.
.@Ixiacom, a @Keysight Business, Enhances Active Network Monitoring Platform with Machine Learning. Hawkeye leverages #machinelearning to identify and troubleshoot problems to help enterprises shorten network outages and improve uptime #NetOps #NPM
As the volume and velocity of raw network and application data continues to increase, network operations teams are faced with a flood of alerts. These teams need to reduce alert fatigue and increase their ability to troubleshoot network and application issues. In response, machine learning has emerged as an innovative way to gain insights from vast amounts of data. “By 2022, over 50 percent of new enterprise applications developed will incorporate machine learning or artificial intelligence models,” according to Gartner.
“Keysight’s Hawkeye utilizes the power of machine learning to help network operations teams make sense of their increasingly complex networks,” said Recep Ozdag, vice president and general manager of visibility at Keysight’s Network Applications & Security Group (formerly Ixia Solutions Group). “Network operations teams struggle to correlate raw performance metrics with actual network problems. Hawkeye’s new machine learning capabilities offer insight into meaningful variations enabling these teams to quickly be alerted to real outages, congestion and application performance issues.”
Hawkeye features automatic threshold and outlier detection which combines machine learning-based problem detection with customizable sensitivity criteria. It cuts through clutter and immediately notifies network operations teams of potential problems. An outlier dashboard enables these teams to easily see potential problems in one place offering built-in, drill down visualizations that assist with root cause analysis and resolution.
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