As　a　new　computing　paradigm,　cloud　computing　is　receiving　considerable　attention　in　both　industry　and　academia.　Task　scheduling　plays　an　important　role　in　large-scale　distributed　systems.　However,　most　previous　work　only　consider　cost　or　makespan　as　optimized　objective　for　cloud　computing.　In　this　paper,　we　propose　a　soft　real-time　task　scheduling　algorithm　based　on　particle　swarm　optimization　approach　for　cloud　computing.　The　optimized　objectives　include　not　only　cost　and　makespan,　but　also　deadline　missing　ratio　and　load　balancing　degree.　In　addition,　to　improve　resource　utilization　and　maximize　the　profit　of　cloud　service　provider,　a　utility　function　is　employed　to　allocate　tasks　to　machines　with　high　performance.　Simulation　results　show　the　proposed　algorithm　can　effectively　minimize　deadline　missing　ratio,　maximize　the　profit　of　cloud　service　provider　and　achieve　better　load　balancing　com-　pared　with　baseline　algorithms.　©　Springer　International　Publishing　Switzerland　2015.