Optimizing Alerts on Free space on disks using Machine Learning - OpsClarity

The available space on the disk (diskfree) has a significant and often catastrophic impact on applications and services running on the system. For this reason, every DevOps engineer knows that it is crucial to carefully monitor disk usage in all critical systems, especially ones that tend to rapidly use up disk space, such as heavily used Hadoop stores, applications with extensive logging, Kafka clusters with a long retention period, etc. The most common monitors used for diskfree metrics rely on a static threshold where the threshold is set by a DevOps engineer with intricate knowledge of the system and applications running on the system. For example, a DevOps engineer may choose to set a static threshold at 5%, i.e., the monitor will trigger an alert if diskfree falls below 5%. In our experience, this approach is inefficient for several reasons.