Is your feature request related to a problem? Please describe
We do not have a good understanding of how good the resource prediction of the predicted scaling.
Currently we have implemented prediction of scaling using linear regression and power law regression.
We use origo as a point of reference when computing the linear or power law relation of the scaling of pipeline duration, cpu consumption and memory usage. This way, only one dryRun is needed to get a prediction of scaling. Of course, more dryRuns can be added to increase the number of points for the scaling.
Describe the solution you'd like
To establish a method that computes the quality of the regression prediction.
Describe alternatives you've considered
1.1 SST, SSR and SSER
These are common statistics measures for the error of regression.
This could be the basis of an analysis of the regression we are making as a function of how many dryRuns we are adding.
SST (Sum of Squares Total), SSR (Sum of squared residuals), SSER (Sum of squares regression error), SST = SSR + SSER
-- Regression analysis
---- dur, cpu, mem
---- y, f(x)
---- SSR = (y - f)^2
---- SST = (y - mean(y))^2
---- SSER = (f - mean(y))^2
---- SST = SSR + SSER
---- MSE = sqrt( SSR / (n - p) )
Introduction to statistics
statistics tutorial
Is your feature request related to a problem? Please describe
We do not have a good understanding of how good the resource prediction of the predicted scaling.
Currently we have implemented prediction of scaling using linear regression and power law regression.
We use origo as a point of reference when computing the linear or power law relation of the scaling of pipeline duration, cpu consumption and memory usage. This way, only one dryRun is needed to get a prediction of scaling. Of course, more dryRuns can be added to increase the number of points for the scaling.
Describe the solution you'd like
To establish a method that computes the quality of the regression prediction.
Describe alternatives you've considered
1.1 SST, SSR and SSER
These are common statistics measures for the error of regression.
This could be the basis of an analysis of the regression we are making as a function of how many dryRuns we are adding.
SST (Sum of Squares Total), SSR (Sum of squared residuals), SSER (Sum of squares regression error), SST = SSR + SSER
-- Regression analysis
---- dur, cpu, mem
---- y, f(x)
---- SSR = (y - f)^2
---- SST = (y - mean(y))^2
---- SSER = (f - mean(y))^2
---- SST = SSR + SSER
---- MSE = sqrt( SSR / (n - p) )
Introduction to statistics
statistics tutorial