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This documentation refers to a previously released version of BMC Discovery.
See the information on this topic for the latest version (11.2) or version 11.1.

BMC Discovery enables you to discover cluster instances and their configuration in your IT environment. BMC Discovery can discover configuration data for various clustering technologies, such as Microsoft Cluster Server, Symantec Veritas Cluster Server, and so on.

With previous releases of BMC Discovery, you could discover members in the clustering environment, and the clustering software that manages the clustering environment. However, with BMC Discovery 11.0, you can now discover data about the number of members in the cluster, the currently active member in the cluster, and the software applications that the cluster manages.

The following table lists the clustering technologies that BMC Discovery scans and the patterns that are required to discover these technologies. Additional clustering technologies will be added to in upcoming TKU releases. For information about TKU cluster coverage, see the Configipedia page, Cluster Awareness.

Clustering technologyPattern name

HP ServiceGuard Cluster Server

HP.ServiceGuard
Microsoft Cluster Server
Microsoft.MSCluster
Oracle Clusterware
Oracle.Clusterware.Clusterware
Oracle Data Guard
Oracle.ActiveDataGuard
Symantec VERITAS Cluster Server
Symantec.VERITAS.ClusterServer

VMware ESX and ESXi Clusters (using vCenter)

 VMware.VirtualCenter.vCenterClusterConfiguration 

BMC Discovery 11 new clustering model

BMC Discovery 11.0 changes how clusters are modeled. In previous versions, the software running on clusters was modeled as SIs running on each host in the cluster. In version 11, the software running on a cluster is modeled as a single SI running on the cluster itself. At the time of release not all TKU patterns have been updated to build this new model. As updated patterns are activated and used to discover existing clusters, they will automatically change the inferred model. When you scan a host, which is a member of a cluster that has software recognized by one of these updated patterns, the system perform the following steps:

  • creates a new SI linked to the cluster (rather than the host),
  • remembers the value of the type attribute of the created SI,
  • searches all hosts in the cluster for SIs that have the same type attribute value and deletes them.

Requirements for cluster discovery

To perform a full discovery of your clustering environment, BMC Discovery requires credentials depending on the method that is used to discover a specific clustering technology. For example, to discover a Microsoft Cluster Server, see Configuring Windows discovery. For other host systems, see Configuring credentials.

Nodes and relationships

BMC Discovery can now discover and model detailed information about clusters by using technology-specific methods such as WMI for Microsoft Windows clustering and the command line for Veritas clustering. When BMC Discovery finds a host that is a member of a clustering environment, a Cluster node is created. Furthermore, details such as the number of members in the cluster, services managed by the cluster, and the resources shared by the cluster are also discovered. The information that is discovered is modeled using the following nodes:

  • ClusterMember—Represents a host member in your clustering environment, The node contains the IP address of the member. The address is used to link the ClusterMember to a host.
  • ClusterService—Represents a service that is managed by the given cluster. The node contains the IP addresses and DNS names; this information is used by clients of the service for access purposes.
  • ClusterResource—Represents finer details about the configuration of the clustered service. This node is a shared storage that is used by the service or by the software used to implement the service. For example, the clustered Microsoft SQL Server service contains a SQL Server resource and a SQL Server Agent resource. Each instance of the ClusterResource node can contain any number of attributes, which will later be used to match SIs to ClusterServices.

    The following illustration shows how the clustering information is modeled.

 

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