Glossary

95th percentile monitoring

95th percentile monitoring relates to bandwidth. This statistic is useful in measuring data throughput because it more accurately reflects the required capacity of the monitored link for applications that are bandwidth sensitive. The 95th percentile says that 95 percent of the time, the bandwidth usage is below this amount. The remaining 5 percent of the time, the bandwidth usage is above that amount.

baseline averages

Depending on the amount of polled data that is collected, baseline averages are calculated in two ways:

Initially, as an average of the hourly averages for the same hour (regardless of day).
After enough data is collected, as an average of the hourly averages for the same day of the week, same hour.

Baseline averages help to characterize past performance for selected monitored metrics, and helps to assess present performance. Baseline averages and related standard deviations are continually calculated as each hour passes. The standard deviation provides a statistical indicator of how much variability exists in the population data that factored into the baseline average calculations.

In Data Aggregator, what is considered to be "normal" for a specified duration within a window of time is based on the calculated baseline average.

Data Collector

Data Collector coordinates data collection and actively polls for data that is used for reporting and event analysis. Operational metrics and configuration data are polled on discovered devices and their monitored components. The collected data is passed through Data Aggregator and is stored in Data Repository.

device collection

A device collection is a logical grouping of monitored devices, such as servers or routers.

Discovery profile

A discovery profile specifies how inventory discovery operates, including the IP addresses, IP address ranges, and host names that are used to locate your devices.

factory

The term "factory" in Data Aggregator describes items that CA Technologies provides and are often installed with the product. For example, Data Aggregator provides factory vendor certifications, monitoring profiles, and more. These out-of-the-box items can help you get Data Aggregator operational upon installation. They can also serve as examples for creating or importing custom versions of the same item. Mostly, Data Aggregator users cannot edit these factory items.

item

An item can be a device, component, or an interface that Data Aggregator monitors.

metric family

A metric family defines the set of values to collect and report on for a given technology. These values are normalized so that reporting is uniform regardless of the data source. When included in a monitoring profile, metric families determine which values to collect for the devices that are associated with that monitoring profile.

monitoring profile

A monitoring profile is associated with a collection of devices to specify the information to poll and the polling rate. These parameters are applied to each device in the device collection. A selection of default monitoring profiles that are based on types of devices such as routers, switches, and servers is provided.

The monitoring profile also contains the event rules that are applied to each device item in the associated device collection. Rule evaluations occur on each device item in the device collection, and on each metric that you specify in the event rules. These rule evaluations generate either raised or clear events. These events are then sent to Event Manager in CA Performance Center, CA Spectrum, and to CA Performance Center Notifier for further action.

rollup

A rollup is the process during which metric values are aggregated. In an hourly rollup, the 1 minute, 5 minute, 15 minute, 30 minute, and 60 minute polled values for metrics are aggregated every hour. In a daily rollup, hourly values for metrics are aggregated once a day. In a weekly rollup, daily values for metrics are aggregated once a week.

standard deviation

The standard deviation shows how much variation there is from the average (mean, or expected value). A low standard deviation indicates that the data points tend to be very close to the mean. High standard deviation indicates that the data points are spread out over a large range of values.