A　large　number　of　services　provided　by　cloud/edge　computing　systems　have　become　the　most　important　part　of　Internet　services.　In　spite　of　their　numerous　benefits,　cloud/edge　providers　face　some　challenging　issues,　e.g.,　inaccurate　prediction　of　large-scale　workload　and　resource　usage　traces.　However,　due　to　the　complexity　of　cloud　computing　environments,　workload　and　resource　usage　traces　are　highly-variable,　thus　making　it　difficult　for　traditional　models　to　predict　them　accurately.　Traditional　models　fail　to　deal　with　nonlinear　characteristics　and　long-term　memory　dependencies.　To　solve　this　problem,　this　work　proposes　an　integrated　prediction　method　that　combines　Bi-directional　and　Grid　Long　Short-Term　Memory　network　(BG-LSTM)　models　to　predict　workload　and　resource　usage　traces.　In　this　method,　workload　and　resource　usage　traces　are　first　smoothed　by　a　Savitzky-Golay　filter　to　eliminate　their　extreme　points　and　noise　interference.　Then,　an　integrated　prediction　model　is　established　to　achieve　accurate　prediction　for　highly-variable　traces.　Using　real-world　workload　and　resource　usage　traces　from　Google　cloud　data　centers,　we　have　conducted　extensive　experiments　to　show　the　effectiveness　and　adaptability　of　BG-LSTM　for　different　traces.　The　performance　results　well　demonstrate　that　BG-LSTM　achieves　better　prediction　results　than　some　typical　prediction　methods　for　highly-variable　real-world　cloud　systems.