Cloud　computing　providers　face　several　challenges　in　precisely　forecasting　large-scale　workload　and　resource　time　series.　Such　prediction　can　help　them　to　achieve　intelligent　resource　allocation　for　guaranteeing　that　users’　performance　needs　are　strictly　met　with　no　waste　of　computing,　network　and　storage　resources.　This　work　applies　a　logarithmic　operation　to　reduce　the　standard　deviation　before　smoothing　workload　and　resource　sequences.　Then,　noise　interference　and　extreme　points　are　removed　via　a　powerful　filter.　A　Min–Max　scaler　is　adopted　to　standardize　the　data.　An　integrated　method　of　deep　learning　for　prediction　of　time　series　is　designed.　It　incorporates　network　models　including　both　bi-directional　and　grid　long　short-term　memory　network　to　achieve　high-quality　prediction　of　workload　and　resource　time　series.　The　experimental　comparison　demonstrates　that　the　prediction　accuracy　of　the　proposed　method　is　better　than　several　widely　adopted　approaches　by　using　datasets　of　Google　cluster　trace.　©　2020　Elsevier　B.V.