A two-stage preference driven multi-objective evolutionary algorithm for workflow scheduling in the Cloud

The workflow scheduling problem considered difficult in the Cloud becomes even more challenging when multiple scheduling criteria are used for optimization. It is much harder to maximize the conflicting interests of users, service providers and the environment, and to obtain "triple win"solutions. This paper presents a two-stage preference driven multi-objective evolutionary algorithm (tsp-MOEA) to address the workflow scheduling problem involving multiple roles. We formulate it as a multi-objective optimization problem with preferences for the first time by considering the special preferences of different participants in decision-making. In particular, a preference distance strategy is introduced in the first stage to speed up the arrival of solutions around the decision makers' region of interest (ROI); and a special preference region ranking strategy is also designed to focus on searching in the ROI in the second stage. Moreover, the two-stage transition is completed adaptively, which can more fully find a variety of elite solutions. In addition, an elite learning strategy is adopted to guide the global evolution of the population to further enhance the quality of the solution. Extensive experiments demonstrate that the proposed tsp-MOEA approach outperforms recent state-of-the-art algorithms, and can provide significantly better solutions for scientific workflow scheduling in the Cloud.