With　their　fast　development　and　deployment,　a　large　number　of　cloud　services　provided　by　distributed　cloud　data　centers　have　become　the　most　important　part　of　Internet　services.　In　spite　of　numerous　benefits,　their　providers　face　some　challenging　issues,　e.g.,　dynamic　resource　scaling　and　power　consumption.　Workload　prediction　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　paper　proposes　an　integrated　prediction　method　that　combines　the　Savitzky-Golay　filter　and　wavelet　decomposition　with　stochastic　configuration　networks　to　predict　workload　at　the　next　time　slot.　In　this　approach,　a　task　time　series　is　first　smoothed　by　the　SG　filter,　and　the　smoothed　one　is　then　decomposed　into　multiple　components　via　wavelet　decomposition.　Based　on　them,　an　integrated　model　is,　for　the　first　time,　established　and　can　well　characterize　the　statistical　features　of　both　trend　and　detailed　components.　Experimental　results　demonstrate　that　it　achieves　better　prediction　results　and　faster　learning　speed　than　some　representative　prediction　methods.　Note　to　Practitioners-Workload　prediction　plays　an　important　role　in　constructing　scalable　and　green　distributed　cloud　data　centers.　This　paper　presents　a　novel　and　fundamental　methodology　to　achieve　accuracy　and　fast　learning　for　workload　prediction.　It　develops　an　integrated　prediction　approach　that　combines　the　Savitzky-Golay　filter　and　wavelet　decomposition　with　stochastic　configuration　networks　to　predict　workload　at　the　next　time　slot.　In　order　to　establish　a　fine　prediction　model　for　the　obtained　information　while　achieving　better　prediction　results　and　faster　learning　speed,　this　paper　proposes　an　integrated　method,　SGWS,　to　build　a　prediction　model　of　a　task　time　series　and　determine　its　optimal　model　parameters.　The　experimental　results　in　the　real-world　data　set　show　that　the　proposed　method　outperforms