Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds

Cloud computing has been widely regarded as a capable solution for big data processing. 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 a variety of algorithms to help the users to schedule their big data processing workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithms were evaluated by extensive simulation experiments with diverse experimental settings.