In cloud computing

tasks in a job often need to run on different datacenters due to the input data locality or special preference for resources

that is

the job runs across geo-distributed sites.The different tasks in a job have to be scheduled in different domain (data center) to execute for their personalization requirements

so the job completion time depends on the slowest task

which is called barrel effect.As geo-distributed scheduling strategy without regard to heterogeneous resources leads too long execution time span

this dissertation proposes an optimization strategy for geo-distributed scheduling named MIN-Max-Min.The strategy gives priority to select the expectation shortest completion job to execute by heuristic rule.Experiments show that compared with first come first service strategy

the strategy can reduce cross domain average execution time span to less than 40% under the simulation load.