In　recent　years,　cloud　computing　has　witnessed　widespread　applications　across　numerous　organizations.　Predicting　workload　and　computing　resource　data　can　facilitate　proactive　service　operation　management,　leading　to　substantial　improvements　in　quality　of　service　and　cost　efficiency.　However,　these　data　often　exhibit　non-linearity,　high　volatility,　and　interdependencies　across　different　categories,　presenting　challenges　for　accurate　forecasting.　Consequently,　there　is　a　critical　need　to　develop　a　method　that　thoroughly　and　comprehensively　analyzes　all　available　data　to　forecast　future　trends　effectively.　This　work　proposes　a　novel　integrated　data-enhanced　prediction　model　named　SVAPI　for　achieving　high-accuracy　workload　prediction　in　cloud　computing　systems.　SVAPI　employs　the　Savitzky-Golay　filter,　Variational　mode　decomposition,　and　the　mode　selection　based　on　Amplitude-aware　Permutation　entropy　for　feature　processing,　whose　features　are　subsequently　utilized　by　Informer　for　multivariate　joint　analysis　of　the　enhanced　data,　achieving　high-precision　prediction.　Ablation　and　comparative　experiments　with　advanced　prediction　models　are　conducted　on　the　Google　cluster　trace　and　other　typical　datasets.　Realistic　data-driven　results　indicate　that　SVAPI　improves　the　prediction　accuracy　by　37.7%　compared　to　the　original　Informer,　with　each　module　contributing　to　the　performance　enhancement.　Furthermore,　compared　with　Autoformer,　SVAPI　enhances　the　prediction　accuracy　of　workload,　CPU,　and　memory　by　65.6%,　66.9%,　and　70.8%,　respectively,　demonstrating　that　SVAPI　owns　strong　abilities　in　noise　filtering,　feature　processing,　and　multivariate　joint　analysis　for　achieving　higher　prediction　accuracy.　　©　2014　IEEE.