The　economy　of　scale　provided　by　cloud　attracts　a　growing　number　of　organizations　and　industrial　companies　to　deploy　their　applications　in　cloud　data　centers　(CDCs)　and　to　provide　services　to　users　around　the　world.　The　uncertainty　of　arriving　tasks　makes　it　a　big　challenge　for　private　CDC　to　cost-effectively　schedule　delay　bounded　tasks　without　exceeding　their　delay　bounds.　Unlike　previous　studies,　this　paper　takes　into　account　the　cost　minimization　problem　for　private　CDC　in　hybrid　clouds,　where　the　energy　price　of　private　CDC　and　execution　price　of　public　clouds　both　show　the　temporal　diversity.　Then,　this　paper　proposes　a　temporal　task　scheduling　algorithm　(TTSA)　to　effectively　dispatch　all　arriving　tasks　to　private　CDC　and　public　clouds.　In　each　iteration　of　TTSA,　the　cost　minimization　problem　is　modeled　as　a　mixed　integer　linear　program　and　solved　by　a　hybrid　simulated-annealing　particle-swarm-optimization.　The　experimental　results　demonstrate　that　compared　with　the　existing　methods,　the　optimal　or　suboptimal　scheduling　strategy　produced　by　TTSA　can　efficiently　increase　the　throughput　and　reduce　the　cost　of　private　CDC　while　meeting　the　delay　bounds　of　all　the　tasks.