This topic contains the following sections:
Metrics in BMC TrueSight Capacity Optimization are definitions of time-varying information about entities, either domains, systems, or business drivers. (See Entity Types for the various types of entities in BMC TrueSight Capacity Optimization.) The actual information, in the form of metric instances, is loaded into BMC TrueSight Capacity Optimization by connectors.
Metrics are of three sub-types:
BMC TrueSight Capacity Optimization contains definitions of thousands of metrics, sometimes called resource counters, and also, a connector may define its custom metrics. Every metric known to a BMC TrueSight Capacity Optimization instance must have a unique string name called the "object" or "resource" identifier, conventionally in an all-capitals and underscores format, for example, CPU_UTIL, CPU_MODEL, and BYDISK_UTIL. As explained in Datasets and metrics, metrics in BMC TrueSight Capacity Optimization are arranged in homogeneous groups called Datasets. For example:
One particular dataset, named OBJREL, contains a single definition for all relationships. For more information about relationships, see Relationships.
Custom metrics created by users or connectors are distinguished by a _C suffix in their name.
Datasets contain only the definitions of metrics. Instances of metrics are created by a connector based on the connector's data source.
Each instance of a metric is associated with only one entity. For example, a connector may create an instance of the time series metric CPU_UTIL and associate it with a particular system named Host1. Usually, there can be only one instance of a metric associated with an entity, as in the example of CPU_UTIL.
But some metrics are sub-object metrics. For such a metric, multiple metric instances may be associated with a single entity, one per sub-object of an entity. By convention, such metrics have names starting with the prefix BY. For example, the time series metric BYDISK_UTIL can have multiple instances for a system, one per disk. The connector that creates the metric instances assigns strings to each disk of each system, based on the data source. For example, if a system Host1 has two disks named diskA and diskB, then the connector may choose to create two instances of the time series metric BYDISK_UTIL, one instance for a sub-object of Host1 called diskA, and another instance for a sub-object of Host1 called diskB.
When looking at the metric instances of an entity in the BMC TrueSight Capacity Optimization console workspace, you will see the metric and the sub-object of each metric instance identified in columns named Resource and Subresource, respectively.
There is another way to associate multiple metric instances with a single entity: every metric also has a LOCATION field, by default filled with the string UNKNOWN. But when creating an instance, a connector may fill this field with arbitrary strings like Boston or New York. Then each such LOCATION value added, creates a separate instance of the metric associated with the same entity.
The intent of this field is to enable the connector to associate separate instances with geographic locations. Such a scheme might make sense for business drivers, for example, BYPAGE_RESPONSE_TIME could denote response times broken down by web page as well as by the location from where the page request was made. In our example, these two instances would be created for a single business driver entity:
This use of the LOCATION field in metrics is rare, but it is available for when there are an unpredictable number of geographic locations that need to be tracked separately for a single business driver. You can achieve the same effect by creating separate business drivers, one for each location.
When looking at the metric instances of an entity in the BMC TrueSight Capacity Optimization console workspace, you will see the LOCATION value identified in a column named Location.
Assigning a LOCATION field creates a new instance of the metric on the same entity! Do not use the LOCATION field if you simply need to associate a location with a system. Use a configuration metric for that purpose.
Domains, i.e., entities of category APP, may have only configuration series metrics associated with them. Otherwise, domains behave just like the other two categories of metrics, namely systems (SYS) and business drivers (WKLD), for metric instance creation.
The dataset named APPCNF contains definitions for a handful of built-in configuration series metrics, and users or connectors can define additional custom configuration series metrics.
A metric instance is identified by the following tuple:
Unique identifier for an entity, for example, a System ID for a system
Whether the entity is a domain ("APP"), a system ("SYS"), or a business driver ("WKLD")
Metric unique name, for example, BYPAGE_RESPONSE_TIME
Sub-object name, for example, www.foo.com/index.html
Location field, for example, "Boston"
Each of these elements can be specified by a connector. The most typical case is a system time series or configuration series metric, where the SUBOBJECT is GLOBAL and LOCATION is UKNOWN.
Time series metric values are summarized by the BMC TrueSight Capacity Optimization data warehouse to the roll table according to the meanings of the metrics. The meaning of the metrics is also used for choosing the default statistics in the Workspace > Analysis’ charts. Time aggregation from hourly to daily and monthly are not based on the metric types defined in the table below.
Metric type ID
|A count of events, absolute number|
A numeric value that indicates the count of events. For example, errors, transactions, and calls.
Pages downloaded, errors
A numeric value that indicates the time required for a specific action or task to complete.
|A frequency, in events/sec|
A numeric value that indicates events or operations that occur per second.
Numeric metric expressed in percentage.
|Burst percentage, Memory|
A numeric or textual value which indicates a configuration detail that does not frequently change such as Asset ID, Reference date, or max bandwidth.
Number of CPUs
|Positive accumulation counter|
Same as peak counter.
Disk space used
|Negative accumulation counter|
A counter for which minimum value is of interest. For example, free disk space, free storage space volumes, or CPU.
Disk space free
|Generic counter, absolute value|
A numeric value for counter metrics. For example, power consumption or CPU service units.
CPU queue length
|Generic counter, absolute value, weighted||A numeric value for weighted counter metrics, where the weight is set in the ETL.||WAVG VALUE||Web response time|
|Peak percentage counter||Peak value for percentage metrics.||MAX VALUE||Memory Utilization Peak|
Percentage, CPU Utilization
|Peak counter||A numeric value that indicates positive peak values for events or operations that occur per second.|
|Peak packets received per second|
|12||DELTA||Difference between subsequent samples||A positive numeric value that indicates the difference in value between two samples of a metric.||SUM VALUE||-|
|13||WEIGHTED_PERCENTAGE||Percentage counter, weighted||Numeric value expressed in percentage, that is weighted by a value imported by the ETL.||WAVG VALUE||Events of a specific set, |
weighed by total events expressed in percentage.
|Peak counter||Peak value for counter metrics.|
|Peak download time |
for a web page
The BMC TrueSight Capacity Optimization data warehouse uses the indicated summarization methods to calculate hourly, daily, and monthly values from the raw numbers loaded by connectors. No summarization is done for CONF metrics.
A count of events, absolute number