The　industry　of　data　centers　is　the　fifth　largest　energy　consumer　in　the　world.　Distributed　green　data　centers　(DGDCs)　consume　300　billion　kWh　per　year　to　provide　different　types　of　heterogeneous　services　to　global　users.　Users　around　the　world　bring　revenue　to　DGDC　providers　according　to　actual　quality　of　service　(QoS)　of　their　tasks.　Their　tasks　are　delivered　to　DGDCs　through　multiple　Internet　service　providers　(ISPs)　with　different　bandwidth　capacities　and　unit　bandwidth　price.　In　addition,　prices　of　power　grid,　wind,　and　solar　energy　in　different　GDCs　vary　with　their　geographical　locations.　Therefore,　it　is　highly　challenging　to　schedule　tasks　among　DGDCs　in　a　high-profit　and　high-QoS　way.　This　work　designs　a　multiobjective　optimization　method　for　DGDCs　to　maximize　the　profit　of　DGDC　providers　and　minimize　the　average　task　loss　possibility　of　all　applications　by　jointly　determining　the　split　of　tasks　among　multiple　ISPs　and　task　service　rates　of　each　GDC.　A　problem　is　formulated　and　solved　with　a　simulated-annealing-based　biobjective　differential　evolution　(SBDE)　algorithm　to　obtain　an　approximate　Pareto-optimal　set.　The　method　of　minimum　Manhattan　distance　is　adopted　to　select　a　knee　solution　that　specifies　the　Pareto-optimal　task　service　rates　and　task　split　among　ISPs　for　DGDCs　in　each　time　slot.　Real-life　data-based　experiments　demonstrate　that　the　proposed　method　achieves　lower　task　loss　of　all　applications　and　larger　profit　than　several　existing　scheduling　algorithms.　Note　to　Practitioners-This　work　aims　to　maximize　the　profit　and　minimize　the　task　loss　for　DGDCs　powered　by　renewable　energy　and　smart　grid　by　jointly　determining　the　split　of　tasks　among　multiple　ISPs.　Existing　task　scheduling　algorithms　fail　to　jointly　consider　and　optimize　the　profit　of　DGDC　providers　and　QoS　of　tasks.　Therefore,　they　fail　to　intelligently　schedule　tasks　of　heterogeneous　applications　and　allocate　infrastructure　resources　within　their　response　time　bounds.　In　this　work,　a　new　method　that　tackles　drawbacks　of　existing　algorithms　is　proposed.　It　is　achieved　by　adopting　the　proposed　SBDE　algorithm　that　solves　a　multiobjective　optimization　problem.　Simulation　experiments　demonstrate　that　compared　with　three　typical　task　scheduling　approaches,　it　increases　profit　and　decreases　task　loss.　It　can　be　readily　and　easily　integrated　and　implemented　in　real-life　industrial　DGDCs.　The　future　work　needs　to　investigate　the　real-time　green　energy　prediction　with　historical　data　and　further　combine　prediction　and　task　scheduling　together　to　achieve　greener　and　even　net-zero-energy　data　centers.