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Aim at the phenomenon that the increment and decrement of dynamic objects occur synchronously in the stream computing mode, this paper proposed a fast stream computing method for probabilistic rough set three-way decision. Firstly, it discussed the data mode of single-object increment and decrement updating mode in stream computing. Then, it proposed the reasoning of the three-way decision domains in data increment and data decrement dynamic mode respectively based on the pattern of data variation. Finally, it proposed a three-way decision dynamic incremental and deeremental learning algorithm based on the above theory. The comparison experiments of eight UCI datasets show that the algorithm not only outperforms the classical three-decision algorithm in time consumption, but also has strong stability for the three-way decision thresholds. [ABSTRACT FROM AUTHOR]

For implementing the synchronization optimization of tasks execution efficiency and execution cost, this paper presented a multi-objective scheduling optimization algorithm of DAG tasks in cloud environment. This algorithm defined the multiobjective optimization problem as the trade-off optimal solutions set satisfying Pareto optimal and solved this model by the heuristic method. At the same time, for evaluating the quality of multi-objective trade-off solutions, it designed a evaluation mechanism based on hypervolume method, which could obtain the trade-off scheduling solutions with conflict objectives. Through setting cloud environment and three kinds of synthetic workflow and two kinds of real-world scientific workflow, it constructed some simulation experiments. The results show that, compare with the same type of single objective algorithm and multi-objective heuristic algorithm, this algorithm not only has higher solving quality, but has better trade-off degree of solutions, which can conform to the mode of resource utility and workflow scheduling in real-world cloud. [ABSTRACT FROM AUTHOR]