What’s new

A goal of Citrix is to deliver new features and product updates to customers as and when they are available. New releases provide more value, so there’s no reason to delay updates.

To you, the customer, this process is transparent. Initial updates are applied to Citrix internal sites only, and are then applied to customer environments gradually. Delivering updates incrementally helps to ensure product quality and to maximize the availability.

Sep 13, 2021

Support for the Asia Pacific South region

Citrix Analytics for Performance now supports the Asia Pacific South (APS) region. For more information on the regions supported in Citrix Cloud, see Geographical considerations.

To access Performance Analytics from the APS region,

  1. Choose the Asia Pacific South region while onboarding your tenant to Citrix Cloud.

  2. Fill the Registration for Citrix Analytics for Performance in the APS Plane Podio form for a trial or a paid entitlement to Performance Analytics from your tenant in the APS region. You will be notified by mail upon successful allocation.

  3. Once you log on to Citrix Cloud, select your tenant in the APS region of Citrix Cloud and use the https://analytics-aps.cloud.com URL to access your Citrix Analytics Cloud Service.

For more information on accessing Performance Analytics see Access.

Aug 12, 2021

Client Side statistics: Network Interface Type

The Network Interface Type column is added to the tabular data in the Sessions self-service view. This field provides visibility into the client side network and helps root cause if poor session experience is due to issues at the endpoint device or the client side network. The value of this field is N/A for endpoints running Citrix Workspace app Windows version earlier than 2105. For more information, see the Self-service search for Sessions section.

July 29, 2021

Visibility into most resource consuming processes

Citrix Analytics for Performance provides visibility into processes contributing to high resource consumption. This is an important insight for admins to analyze the impact of these processes on user performance. This feature is available for multi-session OS machines in the Machine Statistics page under the Processes tab. You can choose to view the processes ranked as per CPU Utilization or Memory Consumption. The three most resource consuming processes are displayed with percentage CPU or Memory Peak as selected. Charts plot CPU Utilization or Memory Consumption by the process across the selected time period. This feature requires that you enable the Process Monitoring policy from Citrix Studio.

Process visibility

For more information, see Process visibility.

June 10, 2021

Color coding in Session-based self-service view

Tabular data in the Session-based self-service view is color coded to indicate the excellent, fair, or poor category the metrics belong to. This categorization is based on the individual threshold levels of the metrics. The thresholds are calculated dynamically. For more information, see [How are Dynamic Thresholds calculated?

Similar color coding is applied to the metrics available on expanding the rows in the Session-based self-service view.

Color coding visually aids in focusing on and identifying factors that are contributing to poor performance. It also gives an overview of the performance across various factors for the sessions that have been filtered to be seen in the current view.

Machine Actions and Composite Actions

Citrix Analytics for Performance provides actions you can perform on power managed machines in your Citrix Virtual Apps and Desktops Sites. Admins with Full Administrator access can perform Machine Actions on identified machines. This capability helps simplify the task of admins having to monitor and take a sequence of actions on a machine with performance issues. Machine Actions - start, restart, turn maintenance mode on or off, shut down the machine - are accessible from the Machines Analysis page of the respective machine. Also available are Composite Actions that combine more than one action to help admins bring affected machines back to availability with a single click. This feature avoids admins shifting to other consoles, like the Web Studio or Citrix Director, to perform these actions. The feature is the key to close the loop when it comes to troubleshooting and solving issues related to machine performance.

For more information, see Machine Actions and Composite Actions.

May 12, 2021

Infrastructure Analytics Dashboard - Enhancements

In this release, Citrix Analytics for Performance provides an enhanced Infrastructure Analytics Dashboard to improve visibility into the overall availability of the machines. The new Machine Availability page displays the number of hours machines are available or unavailable across sites and Delivery Groups. Machine Availability displays information about machines that are Available and Unavailable. Available machines are further classified into Ready for use and Active states. Unavailable machines are classified into Unregistered, Failed, and Maintenance states. This information helps determine availability of provisioned machines to serve sessions.

The Machine Availability trend shows the distribution of machines in various states across the selected time period. Also available is the sessions chart plotting the successful and failed sessions. This helps correlate unavailable machines with failed sessions.

The Machine Performance section provides information about the performance of Multi-session OS machines.

Additionally, you can use the custom time selection filter to zoom into the machine availability and machine performance for a specific duration within the selected time period.

For more information, see Infrastructure Analytics.

Apr 23, 2021

Failure Insights - Communication Error

In this release, Citrix Analytics for Performance provides insights into Communication Error as a part of Failure Insights.

The Communication Error subpane lists the number of session failures due to communication errors between the endpoint (where the user launches the session) and the VDA. These errors can occur due to incorrect firewall configurations or other errors on the network path.

The two categories of communication errors are:

  • Endpoint to machine—lists the sessions where communication errors have occurred between the endpoint and the machine.
  • Gateway to machine—lists the sessions where communication errors have occurred between the gateway and the machine.

Additionally, the Communication Error subpane displays the following recommendations to resolve the errors.

  • Check the firewall settings on the machine and gateway
  • Check network connectivity between the machine and gateway

This feature is supported only on Citrix Workspace app 2103 and later.

For more information, see Communication Error.

Feb 2, 2021

Visual Summary in Sessions self-service view

Visual Summary of data is available in the Sessions self-service view. Visual Summary presents raw data in the self-service tables as charts aimed at an improved visibility into the user experience.

Visual Summary

The Visual Summary chart displays session categorization based on the chosen criteria. In addition, you can choose to view the session distribution pivoted on a specific parameter. This view helps identify session performance issues related to the pivots.

Use the visualization to identify patterns in data that can help troubleshoot specific issues.

For more information, see the Self-service search for Sessions section in the Self-service article.

Jan 28, 2021

Overloaded Machines factor

Overloaded resources can cause high latency, high logon duration, and failures resulting in poor user experience. The Overloaded Machines factor, added on the User Experience (UX) factors page, gives visibility into the overloaded resources causing poor experience.

Machines that have experienced sustained CPU spikes, or high memory usage, or both, that have lasted for 5 minutes or more, resulting in a poor user experience in the selected duration are considered to be overloaded.

Overloaded Machines Drilldown

The Overloaded Machines section shows:

  • The number of machines in which CPU or memory usage has impacted at least one poor session.
  • The number of users affected due to the impact of overloaded CPU or memory on the session experience.
  • Breakup of:
    • the number of machines affecting users with poor experience due to overloaded resource.
    • the number of users with poor experience impacted by CPU Spikes and High memory usage.

For more information, see the Overloaded Machines section in the User Experience Factors drilldown article.

  • Clicking the number of overloaded users leads to the Users self-service view filtered to show users whose sessions are affected by the overloaded resources.
  • Clicking the number of overloaded machines leads to the Machines self-service view filtered to show the chosen set of overloaded machines - based on classification, or based on the overloaded resource, CPU, or machine.

The Machines self-service view is enhanced with the Overloaded Machines and Overloaded CPU/Memory facets to help filter machines with overloaded resources. For more information, see Overloaded Machines in the Self-Service Search for Performance article.

This video shows a typical troubleshooting scenario using the Overloaded Machines factor. Overloaded Machines video

Dec 16, 2020

User Experience Dashboard: Session count enhancements

A session breakup panel based on protocol is added to the User Experience dashboard. The breakup brings clarity into the total number of sessions launched on the Site versus the number of sessions analyzed in Performance Analytics.

The panel displays for the selected duration,

  • the total number of unique users in the selected Sites,
  • the total number sessions that have been active,
  • individual HDX, Console, and RDP sessions.

Analytics relevant only to HDX sessions is available on the dashboard. For more information about the various sections on the dashboard, see the User Analytics article.

Performance metrics of all the sessions independent of protocol is available in the Users, and Sessions based self-service views. Use the Protocol facet to filter the results based on the session protocol.

For more information, see the Self-Service Search for Performance article.

User Experience Dashboard: Session classification clarity

Not Categorized users and sessions are displayed as a separate session category on the User Experience dashboard. This category in the User Experience Score, Session Responsiveness, and Session Logon Duration sections helps identify users and sessions that cannot be classified as experiencing excellent, fair, or poor performance. A session might not get classified if it is launched from a machine running an older Workspace app version, or if the session fails during the logon. For more information on specific reasons for Not Categorized sessions in individual sections on the dashboard, see,

Connection information

Connection failures are generally an important cause for performance degradation. Connection related parameters are now available in the Self-service view for Sessions to help identify and troubleshoot connection failures easily. The Self-Service view for Sessions includes Connection Type facet and column. Connection Type has values:

  • internal – if the connection is direct without Gateway
  • external – if the connection is through a Gateway

In addition, Gateway FQDN (for external connections) and Machine Address (for internal connections) are available as columns in the Self-service view for Sessions.

The Connection details are available for Endpoints running Citrix Workspace app version 20.12.0 or later for Windows. For all other endpoints, the Connection type is displayed as N/A.

For more information, see the Self-service search for Sessions article.

Endpoint Information enhancements

Endpoint parameters are added to columns in the Users and Sessions based self-service views, in addition to the existing endpoint facets. This feature helps search users and sessions based on the endpoint parameters like the location, OS, and the Workspace app version. The parameters are also available in exported CSV files. In addition, the location algorithm has been enhanced to return the last known location in cases where the latest location of the endpoint is not resolved.

  • The Users and Sessions self-service view contains the location parameters Endpoint Country (last known), and Endpoint City (last known).
  • The Sessions self-service view contains the location parameters Endpoint Country (last known), and Endpoint City (last known), Workspace app version, and Endpoint OS.

The addition of these columns helps define queries using the endpoint parameters. You can easily identify issues with performance that are endpoint specific like the location, Workspace app version, or OS.

For more information, see the Self-Service Search for Performance article.

Dec 15, 2020

Drilldown into Profile Load Insights

Profile load insights is updated with a drilldown to help identify users who have a poor logon experience due to large profile sizes.

UX Drilldown - Profile load insights

The View the correlation link displays the average profile size of users, calculated using profile sizes of users who have had excellent and fair profile load experience. Users having profile sizes larger than the average are likely to have poor profile load times.

The View analysis link displays users whose profile size is larger than the average in the users based self-service view. Use facets to further filter this data to view users with both large profile size and poor logon duration experience.

The self-service views for both users and sessions include the Profile Load and the Average Profile Size fields. These fields help filter and identify users with large profile load times easily.

For more information, see the Profile load insights section in the User Experience (UX) Factors article.

Dec 11, 2020

Identification of user terminated sessions

Session failures are an important factor affecting user experience in most environments. Hence, its accuracy plays an important role in correctly measuring the overall user experience in the environment.

Identification of user terminated sessions is a step forward in this direction. It identifies sessions voluntarily terminated by users separately from failed sessions. The Launch Status field in the Sessions self-service view shows a User Terminated status, apart from the existing Succeeded, and Failed statuses. Addition of the separate User Terminated status increases the accuracy of the session failure count.

This feature is supported with endpoints running:

  • Citrix Workspace app 20.9.0 or later for Android
  • Citrix Workspace app 20.8.0 or later for iOS
  • Citrix Workspace app 20.8.0 or later for Windows

This feature does not support endpoints running Workspace on the web.

For more information, see Self-Service search for Sessions.

Oct 19, 2020

A Machines based self-service search is now added to the existing Users and Sessions based self-service views in Citrix Analytics for Performance.

The machines based self-service view displays key performance indicators of your Virtual Apps and Desktops machines. The metrics include the machine downtime, the latest consecutive failures, performance indicators of the machine resources (CPU and memory) - the peak usage, and the number of peaks for the selected time period. Overloaded resources can cause session failures, high latency, or high logon duration resulting in poor user experience. This view helps easily troubleshoot the performance issues related to machine resource utilization.

You can access the Machines based self-service view from the Search menu in your CAS service. In the list of services on the Search tab, under the Performance section, select Machines. The Machines based self-service view is also available when you drill down from black hole machines. To access the view, on the User experience dashboard, in the Failure Insights section, click the Black hole machines number.

For more information on the Machines based self-service view, see Self-service search for Machines.

Machine Statistics view

Citrix Analytics for Performance provides a Machine Statistics view. This view displays a correlation between the resource load and the session experience on the selected machine for the selected time period. This information helps you understand if high CPU or memory usage is related to session failures. You can then explain a poor experience in your Virtual Apps and Desktops environment.

To access the Machine statistics page, in the Machines self-service view, click the machine name link.

Key data points available on this page are:

  • Relevant machine attributes, such as the OS, Site, Delivery Group, and downtime of the machine during the last 24 hours.
  • Machine performance statistics related to resource usage, such as CPU and memory peaks, and the number of spikes over the last 24 hours. Also displayed is a trend of the CPU and memory consumption.
  • Session performance statistics, such as the number of session failures, and peak concurrent session count over the last 24 hours. Also displayed are trends of session failures and session classification.

You can choose to view machine statistics for any 24-hour duration from the last 14 days. The charts are displayed for a default 4-hour time period. A time navigator helps change this time period and also zoom into any duration within the chosen 24-hour time period.

The machine and session performance statistics displayed in the same view help analyze machine resources, their usage pattern and understand if the machine resources have been a possible bottleneck for poor performance.

For more information about this feature, see the Machine Statistics article.

Failure Insights - Black hole machines

Failure Insights in Citrix Analytics for Performance provides insights into session failures that occurred during the chosen time period. This feature is important in helping troubleshoot and resolve session failures faster. It eases the task of admins who need to troubleshoot session failures to improve session availability and hence, the user experience. In this release, Citrix Analytics for Performance provides insights into Black hole machines as a part of Failure Insights.

Some machines in your environment, though registered and appearing healthy might not service sessions brokered to the them, resulting in failures. Machines that have failed to service four or more consecutive session requests are termed as Black hole machines. The reasons for these failures are related to various factors that might affect the machine, such as, insufficient RDS licenses, intermittent networking issues, or instantaneous load on the machine.

The Black hole machines section of Failure Insights shows the number of black hole machines identified in your environment during the selected time period. The presence of black hole machines in the environment impacts session availability. Suggestions to reduce the number of black hole machines in your environment are provided. Clicking the number of black hole machines opens the Machines based self-service view that is filtered to show the black hole machines in your environment during the selected time period. For more information, see Black hole machines.

July 21, 2020

GPO Insights

GPO Insights displays client-side extensions (CSEs) taking the longest processing time during the selected time period. GPO Insights are available in the Session Logon Duration subfactor table. Click the Possible Reasons link in the GPOs row, Insights column.

GPO Insights are based on the analysis of user sessions having high GPO execution times. Increased GPO execution times are due to CSEs with long processing time. Optimizing CSE processing improves the overall session logon experience of the user. Average CSE execution time depends on the number and type of policies applied with it. Review and tune policies associated with CSEs taking the longest processing time as indicated in the GPO insights. Further, consider deleting the ones that are not required. For more pointers to improve the processing time of CSEs, see GPOs.

June 16, 2020

Improved User Experience Score algorithm

The User Experience score calculation algorithm has been improved. The method for quantifying the experience based on the factors - Session Availability, Session Logon Duration, Session Responsiveness, and Session Resiliency has been optimized. Now, more emphasis is laid on the in-session experience factors.

This update results in a more appropriate classification of users having an excellent, a fair, or a poor experience. You might notice more users being classified as having a fair or a poor experience now. The improved score algorithm enables you to correctly identify poor sessions and resolve issues to improve the user experience. Starting June 2020, the new user classification data appears on your User Experience trend. This change does not affect any classification done earlier.

For more information on the User Experience Score calculation, see the User Experience article.

April 23, 2020

Now, you can search events based on the Endpoint Country or City in the self-service view for User and Session performance data. The self-service view for Session performance data also has filters based on the Session Endpoint OS and Endpoint Version.

This information helps analyze if performance issues are localized to a specific geography, endpoint OS, or version. These filters are available for the Citrix Workspace app for Windows version 1912 and later.

For more information about the usage of these filters in self-service search, see Self-Service Search for Performance.

January 10, 2020

Citrix Analytics for Performance - Generally Available

The Citrix Analytics for Performance is a new subscription based offering from the Citrix Analytics service. It allows you to track, aggregate, and visualize key performance indicators of your Citrix Virtual Apps and Desktops environment. You can use it to analyze performance issues of Citrix Virtual Apps and Desktops Sites both on-premises and on Cloud. For more information, see Performance Analytics.