The　tremendous　parallel　computing　ability　of　cloud　computing　encourages　investigators　to　research　its　drawbacks　and　advantages　on　processing　large-scale　scientific　applications　such　as　workflows.　The　current　cloud　market　is　composed　of　numerous　diverse　public　clouds　and　a　local　private　cloud,　and　workflow　scheduling　is　one　of　the　biggest　challenges　on　hybrid　clouds　due　to　the　highly　fragmented　cloud　market　with　respect　to　service　provisions,　pricing　models,　and　bandwidths.　In　this　paper,　we　propose　an　online-scheduling　strategy　for　continuous　submitted　scientific　workflows　on　hybrid　clouds,　which　aims　to　complete　the　deadline-constrained　applications　as　more　as　possible　at　a　lower　price.　Firstly,　a　hierarchical　iterative　application　partition　(HIAP)　algorithm　is　proposed　to　partition　the　application　into　a　set　of　dependent　tasks.　Moreover,　many　online-scheduling　algorithms　cooperated　with　HIAP　are　presented　to　finish　the　workflows　with　a　low　average　payment.　Our　strategy　takes　into　account　the　basic　characteristics　on　hybrid　clouds　such　as　bandwidth　constraints,　data　transfer　cost　and　computational　cost.　Various　well-known　workflows　are　used　for　evaluating　the　multiple　scheduling　algorithms　and　the　results　show　that　the　MLF_ID　approach　can　achieve　a　promising　performance.　Copyright　(C)　2015　John　Wiley　&　Sons,　Ltd.