Many　different　types　of　applications　simultaneously　execute　in　current　data　centers　(DCs).　To　provide　low　cost　and　improved　performance,　each　application　is　typically　deployed　in　distributed　DCs.　Tasks　of　users　around　the　world　first　go　through　Internet　service　providers　(ISPs)　which　deliver　data　between　distributed　DCs　and　users.　However,　capacities　and　bandwidth　cost　of　different　ISPs　vary.　Besides,　energy　cost　of　multiple　DCs　located　in　different　geographical　pi　aces　is　different.　With　the　growth　of　tasks,　the　DC　provider＇s　energy　and　ISP　bandwidth　cost　is　huge　and　continues　to　increase.　Therefore,　due　to　the　energy　and　bandwidth　cost　difference　in　different　geographical　pi　aces,　it　is　highly　difficult　to　minimize　the　DC　provider＇s　total　cost.　Therefore,　to　tackle　the　problem,　this　work　proposes　a　cost-sensitive　task　routing　approach　that　can　jointly　specify　the　optimal　selection　of　available　ISPs　for　the　arriving　tasks,　and　the　optimal　number　of　switched-on　servers　in　each　DC.　Finally,　the　simulation　with　tasks　in　Google＇s　data　center　shows　the　proposed　cost-sensitive　task　routing　approach　can　effectively　decrease　the　DC　provider＇s　cost,　and　raise　system　throughput　in　comparison　with　some　typical　scheduling　method.