With　their　fast　development　and　deployment,　the　cloud　data　center　providing　a　large　number　of　service　which　has　become　the　most　import　service　of　Internet..　In　spite　of　numerous　benefits,　their　providers　face　some　challenging　issues.　Workload　forecasting　plays　a　crucial　role　in　addressing　them.　Accuracy　and　fast　learning　are　the　key　performances.　Its　consistent　efforts　have　been　made　for　their　improvement.　This　work　proposes　an　integrated　forecasting　method　that　combines　Savitzky-Golay　filtering　and　wavelet　decomposition　with　Stochastic　Configuration　Networks　to　get　the　workload　forcast　in　the　next　period.　In　this　study,　we　adopt　Savitzky-Golay　filtering　to　smoothing　a　task　number　sequence,　and　then　the　smoothed　series　is　decomposed　into　multiple　components　by　wavelet　decomposition.　Based　on　them,　integrated　prediction　model　is　for　the　first　time　established　and　the　statistical　characteristics　of　trend　and　detailed　components　can　be　well　characterized.　The　results　of　our　study　demonstrate　that　the　proposed　method　has　better　performance　than　some　typical　methods.　©　2018　IEEE.