The　infrastructure　resources　in　distributed　green　cloud　data　centers　(DGCDCs)　are　shared　by　multiple　heterogeneous　applications　to　provide　flexible　services　to　global　users　in　a　high-performance　and　low-cost　way.　It　is　highly　challenging　to　minimize　the　total　cost　of　a　DGCDC　provider　in　a　market,　where　bandwidth　prices　of　Internet　service　providers　(ISPs),　electricity　prices,　and　the　availability　of　renewable　green　energy　all　vary　with　geographical　locations.　Unlike　existing　studies,　this　paper　proposes　a　spatial　task　scheduling　and　resource　optimization　(STSRO)　method　to　minimize　the　total　cost　of　their　provider　by　cost-effectively　scheduling　all　arriving　tasks　of　heterogeneous　applications　to　meet　tasks＇　delay-bound　constraints.　STSRO　well　exploits　spatial　diversity　in　DGCDCs.　In　each　time　slot,　the　cost　minimization　problem　for　DGCDCs　is　formulated　as　a　constrained　optimization　one　and　solved　by　the　proposed　simulated　annealing-based　bat　algorithm　(SBA).　Trace-driven　experiments　demonstrate　that　STSRO　achieves　lower　total　cost　and　higher　throughput　than　two　typical　scheduling　methods.　Note　to　Practitioners-This　paper　investigates　the　cost　minimization　problem　for　DGCDCs　while　meeting　delay-bound　constraints　for　all　arriving　tasks.　Previous　task　scheduling　methods　do　not　jointly　investigate　the　spatial　diversity　in　bandwidth　prices　of　ISPs,　electricity　prices,　and　the　availability　of　renewable　green　energy.　Therefore,　they　fail　to　cost-effectively　schedule　all　arriving　tasks　of　heterogeneous　applications　during　their　delay-bound　constraints.　In　this　paper,　a　new　method　that　overcomes　the　shortcomings　of　the　existing　methods　is　proposed.　It　is　obtained　by　using　the　proposed　SBA　that　solves　a　constrained　optimization　problem.　Simulation　results　demonstrate　that　compared　with　two　typical　scheduling　methods,　it　increases　the　throughput　and　decreases　the　cost.　It　can　be　readily　implemented　and　integrated　into　real-world　industrial　DGCDCs.　The　future　work　needs　to　investigate　the　indeterminacy　of　renewable　energy　and　the　uncertainty　in　arriving　tasks　with　rough　deep　neural　network　approaches　on　STSRO.