Abstract
We study a fundamental problem of how to schedule complex workflows in the cloud for applications such as data analytics. One of the main challenges is that such workflow scheduling problems involve many constraints, requirements and varied objectives and it is extremely difficult to find high-quality solutions. To meet the challenge, we explore using mixed integer programming (MIP) to formulate and solve complex workflow scheduling problems. To illustrate the MIP-based method, we formulate three related workflow scheduling problems in MIP. They are fairly generic, comprehensive and are expected to be useful for a wide range of workflow scheduling scenarios. Using results from numerical experiments, we demonstrate that, for problems up to certain size, the MIP approach is entirely applicable and more advantageous over heuristic algorithms.
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