A Performance Model to Estimate Execution Time of Scientific Workflows on the Cloud

Scientific workflows, which capture large computational problems, may be executed on large-scale distributed systems such as Clouds. Determining the amount of resources to be provisioned for the execution of scientific workflows is a key component to achieve cost-efficient resource management and good performance. In this paper, a performance prediction model is presented to estimate execution time of scientific workflows for a different number of resources, taking into account their structure as well as their system-dependent characteristics. In the evaluation, three real-world scientific workflows are used to compare the estimated makespan calculated by the model with the actual makespan achieved on different system configurations of Amazon EC2. The results show that the proposed model can predict execution time with an error of less than 20% for over 96.8% of the experiments..