Leveraging　centralized　and　distributed　load　balancing　algorithm　to　jointly　schedule　long　and　short　flows　attracts　much　attention　since　by　classifying　flows,　they　could　reduce　the　completion　times　of　flows　without　losing　the　scalability.　However,　existing　centralized-distributed　joint　algorithms　generally　classify　flows　according　to　a　static　threshold　and　are　unable　to　adapt　to　traffic　dynamics.　More　importantly,　through　over　investigation,　the　overheads　of　the　controller　caused　by　classifying　flows　and　handling　flows　can　be　further　reduced.　In　this　paper,　we　present　a　Flow　Distribution-Aware　Load　Balancing　(FDALB)　mechanism　to　reduce　flow　completion　times　and　achieve　high　scalability.　In　FDALB,　flows　are　split　into　short　flows　and　long　flows　according　to　a　threshold.　The　traffic　of　short　flows　and　long　flows　are　balanced　by　distributed　and　centralized　algorithms　respectively.　To　adapt　to　traffic　dynamics,　we　propose　a　simple　yet　effective　scheme　adaptively　adjusting　the　splitting　threshold.　To　reduce　the　overheads　of　classifying　flows,　FDALB　leverages　end-hosts　to　tag　long　flows,　which　requires　no　changes　in　networking　hardware.　To　further　reduce　the　overheads　of　handling　flows,　we　proposed　a　new　centralized　algorithm　without　requiring　flow　rates　information.　Using　realistic　datacenter　workloads,　we　show　that　FDALB　reduces　the　average　FCT　of　flows　by　up　to　47%　over　ECMP　while　achieves　higher　scalability　than　the　state-of-art　load　balancing　mechanism　Mahout.　(C)　2017　Elsevier　B.V.　All　rights　reserved.