Containerized applications create an exponential increase in alerts and the number of metrics that can be tracked. More complexity means more monitoring to help gain a clear perspective of performance across the entire system.
With so much data, teams are challenged to separate the noise from the insights needed to optimize performance. With complex dependencies, finding the root cause of problems is like trying to find a “needle in a haystack of needles.”
Container monitoring reduces the number of alerts so that you can focus on the most critical alerts. At the same time, these critical alerts can also display actionable information and the workflows needed to quickly address a problem.
Today, even more intelligent alerts, using machine learning and analytics, are being developed for container monitoring solutions to help predict problems with even greater precision. By learning application performance behavior, these solutions can set baselines dynamically to prevent noise and false alarms. By using predictive analytics to identify problems and anomalies, teams can become more proactive in addressing more complex container issues, helping offset any serious performance issues.