Self-learning automated remediation of changes that cause performance degradation of applications

Abstract

Techniques are disclosed for automatic remediation of application performance degradations caused by configuration changes. In one embodiment, a learning module keeps track of application configuration changes and subsequent effects on the application's performance. The learning module creates new potential remediation rules based on correlations between such configuration changes and performance degradations or improvements. The learning module affirms such potential rules if the correlation between the configuration changes and degradations or improvements are repeatedly observed, and vice versa. When subsequent performance degradations are observed, a rule engine, which maintains a set of remediation rules, evaluates the rules to identify configuration changes relevant to the observed performance degradation and determines whether the probability that the configuration changes caused the degradation are greater than a threshold for invoking a remediation action, such as rolling back the configuration changes.