What you need is some sort of time series analysis that takes into account the dependence of each Y value on previous Y values.
A quicker and dirtier approach (because I know very little about time series analysis) might be simple polynomial regression. If its the same basic temporal patterm each year (ie peak in Feb and December, lows in May and August) try fitting a polynomial regression using the day of the year (1 to 365) as the predictor (ie Load~day + day^2 +day^3 etc) this would capture the general swings through the year. You could then add additional predictors (eg year, client numbers, login numbers etc). This ignores the temporal nature of the data, which generally means the model will look a lot more significant than it really is, but it could at least give you a feel for what might be more important predictors of server load.