Current　cloud　data　centers　(CDCs)　provide　highly　scalable,　flexible,　and　cost-effective　services　to　meet　the　performance　needs　of　emerging　applications.　It　is　critical　for　CDC　providers　to　predict　future　incoming　workloads　such　that　they　can　perform　accurate　resource　provisioning　in　CDCs.　Prediction　accuracy　is　important　and　its　improvement　has　been　pursued　in　much　existing　work.　This　work　adopts　two　different　real-life　Google　data　traces,　based　on　which　such　prediction　is　conducted.　Specifically,　this　work　first　gives　a　novel　prediction　mechanism　that　integrates　wavelet　decomposition,　Savitzky-Golay　(SG)　filter,　and　autoregressive　integrated　moving　average　(ARIMA)　to　realize　workload　prediction　in　each　time　interval.　The　time　series　of　the　workload　is　smoothed　with　an　SG　filter　and　further　divided　into　several　components　with　wavelet　decomposition.　Then,　an　integrated　approach　is　developed　to　predict　statistical　trends　and　their　detail　components.　Real-life　trace-driven　experiments　are　done　and　the　results　suggest　that　the　proposed　method　provides　higher　accuracy　of　prediction　than　its　existing　peers.