Accurate　workload　and　resource　prediction　are　critical　to　realizing　proactive,　dynamic,　and　self-adaptive　resource　allocation　for　building　cost-effective,　energy-efficient,　and　green　cloud　data　centers　(CDCs),　providing　satisfactory　quality　services　to　users　and　high　revenue　to　cloud　providers.　However,　it　is　challenging　because　patterns　of　dramatically　increasing　and　large-scale　workload　and　resource　usage　in　CDCs　vary　significantly　with　time.　Current　prediction　methods　often　fail　to　handle　implicit　noise　data　and　capture　nonlinear,　long　and　short-term,　and　spatial　characteristics　in　workload　and　resource　time　series,　thus　leading　to　limited　prediction　accuracy.　To　tackle　these　issues,　this　work　designs　a　novel　prediction　approach　named　VSBG　that　seamlessly　and　innovatively　combines　Variational　mode　decomposition,　Savitzky　Golay,　Bi-directional　long　short-term　memory　(LSTM),　and　Grid　LSTM　to　predict　workload　and　resource　usage　in　CDCs　accurately.　VSBG　innovatively　integrates　variational　mode　decomposition　(VMD)　and　a　Savitzky　Golay　(SG)　filter　in　a　four-step　manner　before　exploring　its　prediction.　VSBG　leverages　VMD　to　divide　non-stationary　workload　and　resource　time　series　into　multiple　mode　functions.　Then,　VSBG　designs　a　quadratic　penalty,　solves　it　with　a　Lagrangian　multiplier,　and　adopts　a　logarithmic　operation　and　the　SG　filter　to　smooth　the　first　mode　function　to　eliminate　noise　interference.　Finally,　VSBG,　for　the　first　time,　systematically　and　simultaneously　captures　depth　and　temporal　characteristics　of　fluctuating　and　complex　time　series　data　with　two　BiLSTM　layers,　between　which　a　GridLSTM　layer　lies,　thereby　accurately　predicting　workload　and　resources　in　CDCs.　Extensive　experiments　with　different　real-world　datasets　prove　that　VSBG　outperforms　a　holistic　set　of　state-of-the-art　algorithms　on　prediction　accuracy　and　convergence　speed.　IEEE