Document collection policies

Action automation and orchestration

For details about cloud document collection actions, see Document Collection Actions.

You can use Action Scripts and third-party orchestrators (such as ServiceNow) for action orchestration.

Scaling sensitivity

Turbonomic uses a percentile of utilization over the specified observation period. This gives sustained utilization and ignores short-lived bursts.

Turbonomic uses these settings to calculate utilization percentiles for Request Units (RUs). It then recommends actions to improve utilization based on the observed values for a given time period.

• Aggressiveness

  Attribute	Default value
  Aggressiveness	95th Percentile

  When evaluating performance, Turbonomic considers resource utilization as a percentage of capacity. The utilization drives actions to scale the available capacity either up or down. To measure utilization, the analysis considers a given utilization percentile. For example, assume a 95th percentile. The percentile utilization is the highest value that 95% of the observed samples fall below. Compare that to average utilization, which is the average of all the observed samples.

  Using a percentile, Turbonomic can recommend more relevant actions. This is important in the cloud, so that analysis can better exploit the elasticity of the cloud. For scheduled policies, the more relevant actions will tend to remain viable when their execution is put off to a later time.

  For example, consider decisions to reduce capacity. Without using a percentile, Turbonomic never resizes below the recognized peak utilization. Assume utilization peaked at 100% just once. Without the benefit of a percentile, Turbonomic will not reduce resources for that document collection.

  With Aggressiveness, instead of using the single highest utilization value, Turbonomic uses the percentile you set. For the previous example, assume a single burst to 100%, but for 95% of the samples, utilization never exceeded 50%. If you set Aggressiveness to 95th Percentile, then Turbonomic can see this as an opportunity to reduce resource allocation.

  In summary, a percentile evaluates the sustained resource utilization, and ignores bursts that occurred for a small portion of the samples. You can think of this as aggressiveness of resizing, as follows:

  • 99th Percentile – More performance. Recommended for critical document collections that need maximum guaranteed performance at all times, or those that need to tolerate sudden and previously unseen spikes in utilization, even though sustained utilization is low.

  • 95th Percentile (Default) – The recommended setting to achieve maximum performance and savings. This assures performance while avoiding reactive peak sizing due to transient spikes, thus allowing you to take advantage of the elastic ability of the cloud.

  • 90th Percentile – More efficiency. Recommended for document collections that can stand higher resource utilization.

  By default, Turbonomic uses samples from the last 14 days. Use the Max Observation Period setting to adjust the number of days.

• Max observation period

  Attribute	Default value
  Max Observation Period	Last 14 Days

  To refine the calculation of resource utilization percentiles, you can set the sample time to consider. Turbonomic uses historical data from up to the number of days that you specify as a sample period. If the document collection has fewer days' data then it uses all of the stored historical data.

  You can make the following settings:

  • Less Elastic – Last 30 Days

  • Recommended – Last 14 Days

  • More Elastic – Last 7 Days or Last 3 Days

  Turbonomic recommends an observation period of 14 days so it can recommend scaling actions more often. Since Azure SQL DB scaling is minimally disruptive, with near-zero downtime, scaling often should not introduce any noticeable performance risks.

  Note:

  For more information about Azure scaling downtimes, see the Azure documentation.

• Min observation period

  Attribute	Default value
  Min Observation Period	None

  This setting ensures historical data for a minimum number of days before Turbonomic will generate an action based on the percentile set in Aggressiveness. This ensures a minimum set of data points before it generates the action.

  Especially for scheduled actions, it is important that resize calculations use enough historical data to generate actions that will remain viable even during a scheduled maintenance window. A maintenance window is usually set for "down" time, when utilization is low. If analysis uses enough historical data for an action, then the action is more likely to remain viable during the maintenance window.

  • More Elastic – None

  • Less Elastic – 7 Days

Scaling target utilization

The utilization that you set here specifies the percentage of the existing capacity that Turbonomic will consider to be 100% of capacity.

To meet a target RU utilization or individual RU targets, the workloads must be charged according to the RU pricing model.

These advanced settings determine how much you would like a scope of workloads to utilize their resources. These are fixed settings that override the way Turbonomic calculates the optimal utilization of resources. You should only change these settings after consulting with Technical Support.

While these settings offer a way to modify how Turbonomic recommends actions, in most cases you should never need to use them. If you want to control how Turbonomic recommends actions to resize workloads, you can set the aggressiveness per the percentile of utilization, and set the length of the sample period for more or less elasticity on the cloud.

Minimum RU capacity for scale down actions

Turbonomic checks this scaling constraint when it recommends scale down actions to ensure that document collections never scale below the specified value.

Be sure to specify a value that is an increment of 100. By design, Azure increases or decreases the number of RUs at any time in increments or decrements of 100 RUs.

In addition to this scaling constraint, Turbonomic also checks the minimum RU limit that Azure calculated. This limit takes precedence over the value specified in the policy. For example, if the minimum RU limit calculated by Azure is 500 while the minimum RU capacity in the document collection policy is 400, Turbonomic will never recommend scaling below 500 RUs.