云计算环境中面向DAG任务的多目标调度算法

为了实现任务执行效率与执行代价的同步优化，提出了一种云计算环境中的DAG任务多目标调度优化算法。算法将多目标最优化问题以满足Pareto最优的均衡最优解集合的形式进行建模，以启发式方式对模型进行求解；同时，为了衡量多目标均衡解的质量，设计了基于hypervolume方法的评估机制，从而可以得到相互冲突目标间的均衡调度解。通过配置云环境与三种人工合成工作流和两种现实科学工作流的仿真实验测试，结果表明，比较同类单目标算法和多目标启发式算法，算法不仅求解质量更高，而且解的均衡度更好，更加符合现实云的资源使用特征与工作流调度模式。

Multi-objective scheduling algorithm of DAG tasks in cloud computing

For implementing the synchronization optimization of tasks execution efficiency and execution cost, a multi-objective scheduling optimization algorithm of DAG tasks in cloud environment is presented. Our algorithm defines the multi-objective optimization problem as the trade-off optimal solutions set satisfying Pareto optimal and solves this model by the heuristic method. At the same time, for evaluating the quality of multi-objective trade-off solutions, a evaluation mechanism based on hypervolume method is designed, which can 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, we construct some simulation experiments. The results show that, compared with the same type of single objective algorithm and multi-objective heuristic algorithm, our 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.