Cloud　computing　is　increasingly　implemented　by　a　growing　number　of　organizations　in　recent　years.　Their　critical　business　applications　are　deployed　in　distributed　cloud　data　centers　(CDCs)　for　fast　response　and　low　cost.　The　ever-increasing　consumption　of　energy　makes　it　highly　important　to　schedule　tasks　efficiently　in　CDCs.　In　addition,　many　factors　in　CDCs,　e.g.,　the　wind　and　solar　energy　and　prices　of　power　grid　have　spatial　differences.　It　becomes　a　challenging　problem　of　how　to　achieve　the　energy　cost　minimization　for　CDCs　in　such　a　market.　This　work　applies　a　G/G/1　queuing　system　to　evaluate　the　optimization　of　servers　in　each　CDC.　Furthermore,　a　single-objective　constrained　optimization　problem　is　given　and　addressed　by　a　proposed　Simulated-annealing-based　Bees　Algorithm　to　yield　a　close-to-optimal　solution.　Based　on　it,　a　Fine-grained　Task　Scheduling　(FTS)　algorithm　is　designed　to　minimize　the　energy　cost　of　CDCs　by　intelligently　scheduling　heterogeneous　tasks　among　distributed　CDCs.　In　addition,　it　also　determines　running　speeds　of　servers　and　the　number　of　switched-on　servers　in　each　CDC　while　strictly　meeting　tasks＇　delay　bounds.　Realistic　data-driven　results　demonstrate　that　FTS　outperforms　its　typical　benchmark　scheduling　peers　in　terms　of　energy　cost　and　throughput.