Cost Optimization for Scheduling Scientific Workflows on Clouds under Deadline Constraints

Nowadays cloud service providers usually offer users virtual machines with various combinations of configurations and prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination under a deadline constraint is becoming more complex for users. The complexity of determining the cost-minimized combination may be resulted from different causes: the characteristics of user applications, and providers' setting on the configurations and pricing of virtual machine. In this paper, we proposed an algorithm with two variants to help the users to schedule their workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithm is evaluated by extensive simulation experiments with diverse experimental settings.