Dynamically　scheduling　the　bandwidth　based　on　the　traffic　variation　is　important　for　a　task　offloading　system.　However,　it　faces　two　challenges.　On　one　hand,　the　time-varying　nature　of　the　offloading　traffic　makes　it　difficult　to　be　predicted　accurately.　On　the　other　hand,　differentiated　mechanisms　are　applied　to　different　offloading　task　types,　which　greatly　complicates　the　behavior　of　the　task　offloading　system.　It　is　hence　difficult　to　estimate　the　performance　metrics　accurately,　especially　when　the　metric　values　are　extremely　small.　To　tackle　this,　we　present　a　double-machine-learning-based　resource　scheduling　(DML-RS)　method　for　task　offloading　traffic　in　this　paper.　The　features　of　DML-RS　are　as　follows:　i)　the　wavelet　transform　and　the　sliding　time　window　are　incorporated　with　the　LSTM　traffic　prediction　model,　which　can　capture　the　periodic　and　volatile　natures　of　the　offloading　traffic　and　hence　improve　the　prediction　accuracy;　ii)　the　logarithmic　converting　is　applied　to　the　ANN　estimation　models,　which　can　improve　the　sensitivity　of　the　ANN　models　to　the　small　values　and　hence　provides　higher　estimation　accuracy.　As　a　result,　DML-RS　can　predict　the　traffic　demand　of　the　next　network　reconfiguration　time　point　and　optimize　the　resource　allocation　based　on　the　performance　estimations　in　advance.　Results　show　that　DML-RS　offers　near-optimal　results　compared　with　the　existing　method.