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Hi Sahil,   I wondered if that was the case.   The only other thing I can think of, which might not be appropriate in your case, is to use the Error Detection configuration to ignore all HTTP errors apart from the 500.   This of course would mean you would lose visibility of the other errors codes, so probably isn't a good option.   Mark ... View more

Hi Sahil,   If you have Analytics, then you could define an Analytics metric with a query similar to this:   SELECT count(*) FROM transactions WHERE transactionName = "BT1" and userExperience = "ERROR" and segments.errorList.errorCode = "500"   And then define your Health Rule based on this metric.   Mark ... View more

Hi Vinay,   The first thing I would check is connectivity from your host to the AppD SaaS controller - is there a proxy server between them for example? The second thing would be to look for any alerts in the controller indicating a license error   Mark ... View more

Hiya,   Standard deviations will work better if you have periods where the range of values is wider than other period - an example might be more varying response times during busy times, as opposed to quieter times. Comparing against a Standard Deviation will reduce false alarms in those scenarios compared with a comparison against baseline percentages.   If the range of values is constant over time, always distributed over the same range, then a Baseline Percentage would work just as well.   To demonstrate this, I would recommend finding an example in your Metric Browser of a series where the baseline changes by a small amount from hour to hour, but the range varies widely - this should also have larger shaded Standard Deviation values. I've attached an example - we don't want the spike at 7:50am to generate an alarm as we know performance can be a little erratic first thing in the morning. It is well over the baseline though.   Mark     ... View more

Hiya,   What are your trigger conditions for the rule? Are they a simple count, or deviation from baseline? If so, you could try setting the rule to look for multiple occurrences in the last 30 minutes. We've found this can substantially reduce the violations created by single spikes.   The process I go through for this is to look at each event, examine the data points in the metric browser that caused the event to trigger, and determine whether it was a true or false positive. From there I try to figure out a rule that wouldn't be triggered by the false positive. It can be a very iterative process....   Mark ... View more