The　emerging　multi-cloud　environments　(MCEs)　empower　the　execution　of　large-scale　scientific　workflows　(SWs)　with　sufficient　resource　provisioning.　However,　due　to　complex　task　dependencies　in　SWs　and　various　cost-performance　of　cloud　resources,　the　SW　scheduling　in　MCEs　faces　huge　challenges.　To　address　these　challenges,　we　propose　an　Online　Workflow　Scheduling　algorithm　based　on　Adaptive　resource　Allocation　and　Consolidation　(OWS-A2C).　In　OWS-A2C,　the　deadline　reassignment　is　first　executed　for　SW　tasks　based　on　the　execution　performance　of　instance　resources,　which　enhances　resource　utilization　from　a　local　perspective　when　executing　an　SW.　Next,　the　execution　instances　are　allocated　and　consolidated　according　to　the　performance　requirements　of　multiple　SWs,　which　improves　resource　utilization　and　reduces　the　total　costs　of　executing　multiple　SWs　from　a　global　perspective.　Finally,　the　SW　tasks　are　dynamically　scheduled　to　the　execution　instances　with　the　earliest-deadline-first　(EDF)　discipline　and　completed　before　their　sub-deadlines.　The　extensive　simulation　experiments　are　conducted　to　demonstrate　the　effectiveness　of　the　proposed　OWS-A2C　on　SW　scheduling　in　MCEs,　which　outperforms　three　baseline　scheduling　methods　with　higher　resource　utilization　and　lower　execution　costs　under　deadline　constraints.