Clinicians report slow logons, IT pinpoints root cause and permanently fixes issue.
The below screen shots were provided by a large non-profit healthcare organization that includes 4 acute care hospitals, over 20 clinics and 5,000 Citrix users. The Healthcare IT team received reports from clinicians about slow logon times. This document describes how the Citrix engineer used Goliath Performance Monitor to pinpoint and troubleshoot the “Citrix is Slow” complaint and implement a fix action that permanently resolved the issue while reducing logon times by more than 80%.
Logon Duration Drill Down Identifies Root Cause
After receiving complaints about long logon times, the Citrix engineer used the session display to sort by logon times and identified a session with a logon time of nearly two minutes (figure 1)– much too long in a hospital setting where clinicians were often logging in to multiple machines as they provided patient care.
Figure 1 – Using sort to quicky find sessions with high logon times.
Unlike other tools that only provide high level summary stage details for the logon process, the engineer was able to see the more than 33+ detailed stages provided by Goliath Performance Monitor – even though the session in question was more than a week old. This unique level of detail allowed him to quickly identify that 100.4 seconds, 86% of the total logon time was spent on the Brokering Client Validation stage (figure 2-1).
In two clicks, the engineer was able to both confirm the issue and pinpoint root cause by identifying the exact Delivery Controllers involved in the brokering and client validation process. The specific detail provided – including the specific delivery controller involved, focused troubleshooting on the true root cause of the problem. The team realized this problem could be solved – and prevented in the future — by allocating more memory to the specific Delivery Controllers during the busy logon hours in the morning.
After making this change, the system showed a reduction of logon times by over 80% during early morning shift hours when most of the logon traffic occurred. In addition, proactive alerts were set to track both logon times as well as memory utilization for the Delivery Controllers involved. This means that if conditions or thresholds are met in the future, IT will receive an alert and take corrective action before users are impacted.