The　skyrocketing　growth　in　types　and　number　of　heterogeneous　applications　dramatically　increases　the　amount　of　energy　consumed　by　distributed　green　data　centers　(DGDCs).　The　spatial　and　temporal　variations　in　prices　of　power　grid　and　availability　of　renewable　energy　make　it　highly　challenging　to　minimize　the　energy　cost　by　intelligently　scheduling　arriving　tasks　of　heterogeneous　applications　among　green　data　centers　while　meeting　their　expected　delay　bound　constraints.　Unlike　existing　studies,　this　work　proposes　a　Spatio-Temporal　Task　Scheduling　(STTS)　algorithm　to　minimize　the　energy　cost　by　cost-effectively　scheduling　all　arriving　tasks　to　meet　their　delay　bound　constraints.　It　well　uses　spatial　and　temporal　variations　to　achieve　DGDC　cost　reduction　and　throughput　improvement.　In　each　time　slot,　the　energy　cost　minimization　problem　for　DGDC　providers　is　formulated　as　a　nonlinear　constrained　optimization　one,　and　addressed　with　the　proposed　Genetic　Simulated-annealing-based　Particle　swarm　optimization.　Trace-driven　experiments　show　that　STTS　achieves　larger　throughput　and　lower　energy　cost　than　several　typical　task　scheduling　approaches　while　strictly　meeting　all　tasks＇　delay　bound　constraints.