Grid computing technologies have made it possible for researchers to get access to more computational resources than ever before.??However, heterogeneity of resources, scheduling policy and applications makes it difficult to manage the execution of jobs in such environments. Themanner in which a job is configured to run on one resource may not be appropriate for another resource. Long running jobs need to be split into a series of shorter jobs with the aid of checkpointing to increase fault tolerance and to meet scheduling policy constraints of different resources. To take full advantage of grid environments, execution management systems need to be able to configure, reconfigure, checkpoint and migrate jobs as necessary.Information models that describe resources, policy and applications help automate execution management and allow for interoperability between tools. This paper presents application and job models that describe the processes required to configure and execute jobs on different resources. Example instances of the application and jobmodels for the GROMACS molecular dynamics simulation example are also presented, illustrating how the models aid in automation.