Abstract:
An application monitoring infrastructure that enables application configuration changes on multiple machines across multiple OS types to be tracked by identifying data containers that are to be monitored for changes, detecting a change to a monitored data container, and storing data representative of a changed version of the monitored data container responsive to detecting that the monitored container was changed. The data containers that are to be monitored for changes are identified from templates, and a unique template is provisioned for each of the applications.
Abstract:
An application monitoring infrastructure that enables application configuration changes on multiple machines across multiple OS types to be tracked by identifying data containers that are to be monitored for changes, detecting a change to a monitored data container, and storing data representative of a changed version of the monitored data container responsive to detecting that the monitored container was changed. The data containers that are to be monitored for changes are identified from templates, and a unique template is provisioned for each of the applications.
Abstract:
An application monitoring infrastructure that enables application configuration changes on multiple machines across multiple OS types to be tracked by identifying data containers that are to be monitored for changes, detecting a change to a monitored data container, and storing data representative of a changed version of the monitored data container responsive to detecting that the monitored container was changed. The data containers that are to be monitored for changes are identified from templates, and a unique template is provisioned for each of the 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.
Abstract:
An application monitoring infrastructure that enables application configuration changes on multiple machines across multiple OS types to be tracked by identifying data containers that are to be monitored for changes, detecting a change to a monitored data container, and storing data representative of a changed version of the monitored data container responsive to detecting that the monitored container was changed. The data containers that are to be monitored for changes are identified from templates, and a unique template is provisioned for each of the applications.