Nowadays,　a　large　number　of　cloud　services　have　been　published　and　hosted　by　geo-distributed　cloud　data　centers　(Geo-2DCs).　In　spite　of　numerous　benefits,　those　Geo-2DCs　face　significant　challenges　such　as　dynamic　resource　scaling　where　workload　forecasting　plays　a　crucial　role　in　addressing　such　a　challenge.　High　accuracy　and　fast　learning　are　key　indicators　for　workload　forecasting　and　the　literature　has　witnessed　a　lot　of　efforts.　This　work　proposes　an　integrated　forecasting　method,　equipped　with　noise　filtering　and　data　frequency　representation,　named　Savitzky-Golay　and　Wavelet-supported　Stochastic　Configuration　Networks　(SGW-SCN),　to　predict　the　amount　of　workload　in　future　time　slots.　In　this　approach,　the　workload　time　series　is　first　smoothed　by　a　Savitzky-Golay　filter　and　then　decomposed　into　multiple　components　via　wavelet　decomposition.　With　stochastic　configuration　networks,　an　integrated　model　is　established　to　characterize　statistical　characteristics　of　both　trend　and　detail　components.　Extensive　results　have　demonstrated　that　the　proposed　method　achieves　higher　forecasting　accuracy　and　faster　learning　speed　than　typical　forecasting　methods.　(C)　2018　Elsevier　Inc.　All　rights　reserved.