PTN-21 Cost Optimization For Deadline-aware Scheduling Of Big-data Processing Jobs On Clouds

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Abstract: 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