An　increasing　number　of　organizations　choose　distributed　green　data　centers　(DGDCs)　and　use　their　infrastructure　resources　to　deploy　and　manage　multiple　applications　that　flexibly　provide　services　to　users　around　the　world　in　a　cost-effective　way.　The　dramatic　growth　of　tasks　makes　it　highly　challenging　to　maximize　the　total　profit　of　a　DGDC　provider　in　a　market,　where　the　revenue,　price　of　power　grid,　solar　radiation,　wind　speed,　the　maximum　amount　of　energy,　on-site　air　density,　and　the　number　of　servers　in　DGDCs　all　vary　with　geographical　sites.　Different　from　existing　studies,　this　paper　designs　a　profit-sensitive　spatial　scheduling　(PS3)　approach　to　maximize　the　total　profit　of　a　DGDC　provider　by　smartly　scheduling　all　tasks　of　multiple　applications　to　meet　their　response　time　constraints.　PS3　can　well　utilize　such　spatial　diversity　of　the　above　factors.　In　each　time　slot,　the　profit　maximization　for　the　DGDC　provider　is　formulated　as　a　constrained　nonlinear　program　and　solved　by　the　proposed　genetic-simulated-annealing-based　particle　swarm　optimization.　Real-life　trace-driven　simulation　experiments　demonstrate　that　PS3　realizes　higher　total　profit　and　throughput　than　two　typical　task　scheduling　methods.　Note　to　Practitioners-This　paper　investigates　the　profit　maximization　problem　for　a　DGDC　provider,　while　the　average　response　time　of　all　arriving　tasks　of　each　application　is　within　their　corresponding　constraint.　Existing　task　scheduling　approaches　fail　to　jointly　consider　the　spatial　variations　in　many　factors,　including　the　revenue,　price　of　power　grid,　solar　radiation,　wind　speed,　the　maximum　amount　of　energy,　on-site　air　density,　and　the　number　of　servers　in　DGDCs.　Consequently,　they　cannot　schedule　all　tasks　of　multiple　applications　within　their　response　time　constraints　in　a　profit-sensitive　way.　In　this　paper,　a　profit-sensitive　spatial　scheduling　(PS3)　method　that　tackles　the　drawbacks　of　previous　approaches　is　presented.　It　is　achieved　by　adopting　a　proposed　genetic-simulated-annealing-based　particle　swarm　optimization　algorithm　that　solves　a　constrained　nonlinear　program.　Simulation　experiments　prove　that　compared　with　two　typical　scheduling　approaches,　it　increases　the　total　profit　and　throughput.　It　can　be　readily　realized　and　incorporated　into　real-life　industrial　DGDCs.　The　future　work　should　improve　the　proposed　method　by　analyzing　the　indeterminacy　in　green　energy　and　the　uncertainty　in　tasks.