Performance montioring solutions provider LogicMonitor today announced a new early warning system feature for their AIOps solution, LM Intelligence. The early warning system is designed to deliver actionable warning indicators to IT administrators when it detects an imminent issue. This aligns with LM Intelligence’s capabilities for helping enterprises predict and prevent hardware and service outages.
“In the same way that a doctor identifies symptoms that if left untreated might lead to major complications, LM Intelligence’s AIOps early warning system will detect the signs that precede IT performance problems, and warn users accordingly,” said Tej Redkar, LogicMonitor’s Chief Product Officer. “96% of IT decision makers report experiencing at least one major outage during the past three years. By providing our customers with AIOps functionality that warns them about issues before they happen, teams can prevent problems and outages instead of reacting to them. This ultimately prevents service-level agreement failure and helps teams deliver a consistently positive customer experience.”
The two core features of the new LogicMonitor early warning system are root cause analysis and dynamic thresholds. Root cause analysis determines the source of performance anomalies and outages, and filters out alerts that aren’t related to the root cause of an issue. Dynamic thresholds define expected performance ranges for an enterprise’s IT systems, which helps the solution understand which performance dips are cause for alarm. With these two functions, LM Intelligence reduces the amount of unimportant noise that administrators and engineers hear, as well as helping them discover actionable insights.
“Linux machines notoriously generate lots of CPU performance alerts. These machines are being highly utilized intentionally and well within their limits, but it’s creating noise,” said Jason Smith, Associate Director at Agio, a LogicMonitor customer. “With dynamic thresholds now part of LM Intelligence, we only get alerted when CPU is truly abnormal.”
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