BMC Discovery components

BMC Discovery automatically discovers the hardware and software in your data center, determines configuration and relationship data, and maps applications to the IT infrastructure. 

The following list defines the components of BMC Discovery:

BMC Discovery Outpost

Information about your organization's hardware and software is obtained by the BMC Discovery Outpost. The BMC Discovery Outpost is application software that runs on a dedicated Windows server in your data center or on a public cloud, and  connects securely to your appliances over HTTPS by using a single, web-friendly port (443). The BMC Discovery appliance sends a request to an BMC Discovery Outpost to scan the IP address required, and the BMC Discovery Outpost accesses the target by using the credentials that are held in its own secure, encrypted vault. The targets are accessed by using a variety of methods, such as SSH, Telnet, WMI, and SNMP. Once logged into a discovery target, the BMC Discovery Outpost executes commands to access the target details, and their results are encrypted and sent to the BMC Discovery appliance. When the BMC Discovery appliance receives the data, it stores it in the datastore as Directly Discovered Data (DDD). 

The BMC Discovery Outpost performs ssh discovery using an API rather than an ssh client. Consequently, alternative ssh clients are not supported on the BMC Discovery Outpost. BMC Discovery Outpost is FIPS compliant.

Multiple BMC Discovery Outposts can be deployed to handle segmented networks, and these can all communicate with a single BMC Discovery appliance. Similarly, the BMC Discovery Outpost can be registered with multiple appliances and receive work from those appliances.

The BMC Discovery Outpost is included as part of monthly TKU releases and is self-updating. The BMC Discovery Outpost periodically checks that it is up to date, and if not, downloads and when the BMC Discovery Outpost is idle, runs the installer. Automatic updated can be disabled, though we recommend against doing so. If you disable automatic updates, you are notified when a new version is available and you should apply the update at the first opportunity. 

Discovery service

The discovery service performs a similar task to the BMC Discovery Outpost but runs on the BMC Discovery appliance. For Windows discovery performed from the (Linux-based) appliance uses an external proxy running on a dedicated windows server to log into and scan Windows hosts.

Discovery queue

Your interaction with BMC Discovery results in the reasoning service placing work on the discovery queue. The discovery service on the appliance, and the BMC Discovery Outposts poll the discovery queue for work to undertake. The results of that work are returned to the discovery queue, where they wait until the reasoning service requests the discovery queue for work. Reasoning effectively asking for the results of the work it previously placed on the queue earlier. 

The discovery queue is conceptually situated between reasoning, and the discovery service and Outposts, but is actually a part of reasoning. If you examine the running services , you will see that there is no discovery queue service. 

Reasoning

The Discovery Engine is supported by Reasoning which intelligently infers information about hosts and programs from the DDD returned. The process of adding DDD to the datastore causes Reasoning to execute patterns against the DDD. Each pattern represents knowledge about particular software or hardware and Reasoning uses this knowledge to create more detailed "inferred" data. Inferred data is the representation of the scanned IT environment and is stored in the datastore. The provenance of each item of inferred data is also stored meaning that when examining an inferred entity in the UI, you can examine the information which was used to create it.

Patterns can be updated, either through monthly Technology Knowledge Update (TKU) releases, or by writing new patterns using The Pattern Language (TPL).

Datastore

The datastore is the database in which the DDD and inferred data is stored. Data written to the datastore is instantly indexed allowing you to search for items of interest using simple keywords in the UI. In addition to the DDD and inferred data mentioned, the datastore holds TKU information, patterns, operational data and some configuration data.

The datastore uses a graph model meaning that it represents data as nodes connected to each other with relationships. This is more suited to modeling the complex relationships in an IT environment than a relational database.

CMDB Synchronization

CMDB synchronization provides a means of keeping data in the BMC Helix CMDB continuously synchronized with information discovered by BMC Discovery. The BMC Discovery data model is different from the Common Data Model (CDM) used in the CMDB, so the synchronization mechanism transforms the required information from one data model to the other.

Start anywhere application modeling

Start anywhere application modeling is a new approach to application modeling, which enables you to choose any entry point, or points into an application, and begin modeling from there. For robust applications, logical entry points differ depending on the view of the user. For example, an application owner might choose where the data is stored as the best entry point, and a user of the application might choose the server to which they connect to access the application. The start anywhere approach also prevents parts of applications from being missed if they are not currently connected to an entry point, such as a URL, which may lead to a load balanced service or web server. You might also choose multiple entry points to model the application.

You should start application modeling from anywhere that is interesting in the context of the application you are modeling. The best way of doing this is the search box in the top right of the UI. Enter the name or other detail of something you know to be in the application, and explore the data from there. When you see what you are looking for, start modeling.

The models produced with start anywhere application modeling are simple to create and work on the basis of data that has already been discovered from you network, data that is held in the BMC Discovery datastore. This does not mean that the models are static, rather, they update automatically to reflect the current data. So, if you scan the application and a new component of the application is discovered, it is automatically linked in to the existing components, and reflected when you view the model again. If the new component is of a type that you have excluded from the model, it is still in the datastore, but not included in the application model.

Clustering

Clustering, also known as Big Discovery, enables you to discover and get usable results from even the largest Data Centers in the shortest time possible with the help of clusters. A cluster consists of two or more coordinated BMC Discovery machines, one of which is in control of the group and is referred to as the coordinator. Additional machines can be added to the cluster at any time. When you do so, the system spreads the existing data out amongst all the machines evenly, in order to maximize performance and utilization of each machine in the cluster. This can take a while to happen, but the system is fully usable while it happens: you can do discovery and browse the data.

You can configure clusters with fault tolerance meaning that if a machine fails, it can be removed and replaced, without any consequent loss of data, and without interrupting normal operation. Managing your cluster has been made as simple as possible. Use any machine in the cluster to manage all others: update TKUs, add credentials and scans, and even upgrade the product itself from a single place. Regardless of data center size or complexity, BMC Discovery delivers a refreshed view of your data center as often as needed. Clustering delivers powerful, actionable data center insight in the shortest possible time:

• Leverages new appliance clustering technology
• No scanning limits – just add hardware
• Transparent cluster management
• Fault tolerance to manage hardware failures
• Highly scalable end-user UI