A　large　number　of　cloud　services　provided　by　cloud　data　centers　have　become　the　most　important　part　of　Internet　services.　In　spite　of　numerous　benefits,　cloud　providers　face　some　challenging　issues　in　accurate　large-scale　task　time　series　prediction.　Such　prediction　benefits　providers　since　appropriate　resource　provisioning　can　be　performed　to　ensure　the　full　satisfaction　of　their　service-level　agreements　with　users　without　wasting　computing　and　networking　resources.　In　this　work,　we　first　perform　a　logarithmic　operation　before　task　sequence　smoothing　to　reduce　the　standard　deviation.　Then,　the　method　of　a　Savitzky-Golay　(S-G)　filter　is　chosen　to　eliminate　the　extreme　points　and　noise　interference　in　the　original　sequence.　Next,　this　work　proposes　an　integrated　prediction　method　that　combines　the　S-G　filter　with　Long　Short-Term　Memory　network　models　to　predict　task　time　series　at　the　next　time　slot.　We　further　adopt　a　gradient　clipping　method　to　eliminate　the　gradient　exploding　problem.　Furthermore,　in　the　process　of　model　training,　we　choose　optimizer　Adam　to　achieve　the　best　results.　Experimental　results　demonstrate　that　it　achieves　better　prediction　results　than　some　commonly-used　prediction　methods.