@@ -64,131 +72,115 @@ the data source header in the RRD.
A second application of the tune function is to set or alter parameters used by the specialized function RRAs for aberrant behavior detection.
The name of the RRD you want to tune.
modify the heartbeat of a data source. By setting this to a high +value the RRD will accept things like one value per day.
alter the minimum value acceptable as input from the data source. +Setting min to 'U' will disable this limit.
alter the maximum value acceptable as input from the data source. +Setting max to 'U' will disable this limit.
alter the type DST of a data source.
rename a data source.
Alter the deviation scaling factor for the upper bound of the confidence band used internally to calculate violations for the FAILURES RRA. The default value is 2. Note that this parameter is not related to graphing confidence bounds which must be specified as a CDEF argument to generate a graph with confidence bounds. The graph scale factor need not to agree with the value used internally by the -FAILURES RRA. +FAILURES RRA.
Alter the deviation scaling factor for the lower bound of the confidence band used internally to calculate violations for the FAILURES RRA. The default value is 2. As with --deltapos, this argument is unrelated to the scale -factor chosen when graphing confidence bounds. +factor chosen when graphing confidence bounds.
Alter the number of confidence bound violations that constitute a failure for purposes of the FAILURES RRA. This must be an integer less than or equal to the window length of the FAILURES RRA. This restriction is not verified by the tune option, so one can reset failure-threshold and window-length -simultaneously. Setting this option will reset the count of violations to 0. +simultaneously. Setting this option will reset the count of violations to 0.
Alter the number of time points in the temporal window for determining failures. This must be an integer greater than or equal to the window length of the FAILURES RRA and less than or equal to 28. Setting -this option will reset the count of violations to 0. +this option will reset the count of violations to 0.
Alter the intercept adaptation parameter for the Holt-Winters +forecasting algorithm. This parameter must be between 0 and 1.
Alter the slope adaptation parameter for the Holt-Winters forecasting +algorithm. This parameter must be between 0 and 1.
Alter the seasonal coefficient adaptation parameter for the SEASONAL +RRA. This parameter must be between 0 and 1.
Alter the seasonal deviation adaptation parameter for the DEVSEASONAL +RRA. This parameter must be between 0 and 1.
Alter the size of the smoothing window for the SEASONAL RRA. This must +be between 0 and 1.
Alter the size of the smoothing window for the DEVSEASONAL RRA. This must +be between 0 and 1.
This option causes the aberrant behavior detection algorithm to reset for the specified data source; that is, forget all it is has learnt so far. Specifically, for the HWPREDICT or MHWPREDICT RRA, it sets the intercept and slope coefficients to unknown. For the SEASONAL RRA, it sets all seasonal @@ -198,9 +190,7 @@ violation history. Note that reset does not erase past predictions (the values of the HWPREDICT or MHWPREDICT RRA), predicted deviations (the values of the DEVPREDICT RRA), or failure history (the values of the FAILURES RRA). This option will function even if not all the listed -RRAs are present. -
Due to the implementation of this option, there is an indirect impact on other data sources in the RRD. A smoothing algorithm is applied to SEASONAL and DEVSEASONAL values on a periodic basis. During bootstrap @@ -211,12 +201,10 @@ for all data sources in the file. This is unlikely to have serious consequences, unless the data being collected for the non-reset data sources is unusually volatile during the reinitialization period of the reset data source.
-Use of this tuning option is advised when the behavior of the data source time series changes in a drastic and permanent manner.