ControlUp Dashboards

This thread walks through a real customer question: how do you find which processes are eating the most CPU across a large VDI estate (200 servers) over a long period, so you know what to optimize? It starts in ControlUp Dashboards. Using the VDI process data, you can build a widget showing top processes by CPU, then narrow it to the machines you care about using global filters (filter by folder, or switch to query filters with IS / IS NOT). A table beats a bar chart for a top-50 view, and any filters and time frames you set are saved in the URL, so you can bookmark or share the exact view. The built-in Gallery dashboards (notably "Big Screen Dashboard VDI") already include a processes-by-CPU widget out of the box. The harder part is the one the customer really cared about: getting a meaningful picture across all machines rather than a single noisy spike on one box. The data is stored per process per 5-minute timeslot, not pre-aggregated per machine, so the practical approach that emerged was: Filter to the folder containing all target machines. Use CPU usage P95 to strip out short, expected spikes (with a fallback to average CPU, since P95 returned N/A in some environments — a possible bug worth a support ticket). Add metrics under Advanced: computer_name (unique count) to see how many machines run the process, and process_name (count) to see how often it appeared (each record ≈ a 5-minute aggregate). The processes worth optimizing are the ones combining high CPU + high machine count + high record count. Worked example: a process showing ~1,080 records across 197 machines roughly translates to ~90 hours of activity over 7 days, or a few minutes per machine per day. The same method surfaced candidates like CompatTelRunner.exe, WerFault.exe, and WEM-related activity for further investigation. To go deeper, the App Trends and App Statistics reports let you drill into a specific app, and product_name data helps identify what's actually behind generic process names like setup.exe. A few important caveats came up: averages hide spikes (10% average could be one minute at 100%), process counts are per machine, and hypervisor-level CPU (e.g., XenServer) can disagree with in-VM agent data when hosts are over-provisioned — which is also why a Sizing Recommendations report may suggest removing vCPUs even on a machine that looks maxed out. Bottom line: use the dashboard to identify the heavy processes (folder filter + P95/avg CPU + machine count + record count), then use the App Trends report to dig into the details per app.

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