Grid technologies have progressed towards a service-oriented paradigm that enables a new way

of service provisioning based on utility computing models, which are capable of supporting diverse

computing services. It facilitates scientific applications to take advantage of computing resources

distributed world wide to enhance the capability and performance. Many scientific applications in areas

such as bioinformatics and astronomy require workflow processing in which tasks are executed based on

their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need

to consider users’ QoS requirements. In this paper, we present a genetic algorithm approach to address

scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and

budget.

Keywords: Grid workflow