Infrastructure　in　Distributed　Green　Data　Centers　(DGDCs)　is　concurrently　shared　by　multiple　different　applications　to　flexibly　provide　a　growing　number　of　services　to　global　users　in　a　cost-effective　way.　A　highly　challenging　problem　is　how　to　maximize　the　total　profit　of　the　DGDC　provider　in　a　market　where　Internet　Service　Provider　(ISP)　bandwidth　price,　availability　of　green　energy,　price　of　power　grid,　and　revenue　brought　by　the　execution　of　tasks　all　vary　with　geographical　locations.　Unlike　existing　studies,　this　article　proposes　a　Geography-Aware　Task　Scheduling　(GATS)　approach　by　considering　spatial　variations　in　DGDCs　to　maximize　the　total　profit　of　the　DGDC　provider　by　intelligently　scheduling　tasks　of　all　applications.　In　each　time　slot,　the　formulated　profit　maximization　problem　is　solved　as　a　convex　optimization　one　via　the　interior　point　method.　Trace-driven　simulations　show　that　GATS　achieves　larger　total　profit　and　higher　throughput　than　two　typical　task　scheduling　approaches.