Abstract: A system displays a set of alerts in an alert view. Each alert indicates a problem associated with a managed resource operating in a storage area network. The system receives a selection of an alert from the alert view, and performs an analysis of managed resource data associated with the managed resource experiencing the alert to identify performance details concerning the alert in relation to the managed resource data. The system then presents the performance details associated with the alert in a performance view. The performance view includes a topology region identifying a location of the managed resource experiencing the alert within the storage area network, and a graph region identifying the alert in relation to managed resource data for the managed resource experiencing the alert.

Abstract: Deployment of new elements in a computer network can result in development of patch kits adapters, and other software and trigger corresponding regressive test efforts. In a storage area network, a method is disclosed for defining a general, extensible model of a manageable entity for use in management of the entity. The method includes obtaining a set of attribute values related to the manageable entity in relation to the storage area network. A deployment engine identifies corresponding entity categories within a storage area network entity model that contains attribute definitions that correspond to respective attribute values in the set of attribute values obtained from the manageable entity. The deployment engine assigns the respective attribute values of the manageable entity to the corresponding respective attribute definitions identified in the model. The deployment engine then determines if all attribute values of the manageable entity have been assigned.

Abstract: Deployment of new elements in a computer network can result in development of patch kits adapters, and other software and trigger corresponding regressive test efforts. In a storage area network, a method is disclosed for defining a general, extensible model of a manageable entity for use in management of the entity. The method includes obtaining a set of attribute values related to the manageable entity in relation to the storage area network. A deployment engine identifies corresponding entity categories within a storage area network entity model that contains attribute definitions that correspond to respective attribute values in the set of attribute values obtained from the manageable entity. The deployment engine assigns the respective attribute values of the manageable entity to the corresponding respective attribute definitions identified in the model. The deployment engine then determines if all attribute values of the manageable entity have been assigned.

Abstract: Mechanisms and techniques are disclosed that maintain managed object data associated with components in a network such as a storage area network by receiving management data associated with components operating in a storage area network and matching the management data to existing objects in a managed object database. For each existing managed object that matches the management data, the system updates object data within the existing managed objects with the matching management data. For management data not matching the existing managed objects in the managed object database, the system creates at least one new managed object containing the non-matching management data.

Abstract: Database performance assessment typically entails logging and capturing utilities which tend to gather large quantities of unwieldy data leading to a cumbersome and indefinite analysis. A system and method for database performance analysis includes periodic sampling of pending database requests, rather than exhaustive monitoring and capturing all database access traffic, to identify areas of contention. A database access queue is periodically sampled, or scanned, to gather a snapshot of pending requests of database transactions. Pending requests are aggregated by an aggregating process which aggregates, the samples with previous samples corresponding to the same transaction. Correlating the aggregated samples identifies transactions which have been pending the longest and identifies tables and segments in the database which have a relatively high number of pending transactions.