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An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds

机译:云中约束工作流调度的自适应多目标进化算法

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The Cloud workflow scheduling is to find proper Cloud resources for the execution of workflow tasks to efficiently utilize resources and meet different user's quality of service requirements. Cloud workflow scheduling is a constrained and NP-complete problem and multi-objective evolutionary algorithms have shown their excellent ability to solve such problem. But most existing works simply use static penalty function to handle constraints which usually result in premature when the constraints become strict. On the other hand, with the search space being more tremendous and chaotic, how to balance the ability of exploring the entire search space and exploiting the important regions during the evolutionary process is increasingly important. In this paper, an adaptive individual-assessment scheme based on evolutionary states is proposed to handle the constraints in multi-objective optimization problems. In addition, the evolutionary parameters are also adjusted accordingly to balance the exploration and exploitation ability. These are distinguishable from most previous studies that directly incorporate multi-objective evolutionary algorithm to search excellent solutions for Cloud workflow scheduling. Experimental results demonstrate the proposed algorithm outperforms other state-of-the-art methods in convergence and diversity, and it also achieves better optimization ability when it is applied to solve Cloud workflow scheduling problem.
机译:云工作流调度旨在为执行工作流任务找到合适的云资源,以有效利用资源并满足不同用户的服务质量要求。云工作流调度是一个受约束且NP完全的问题,多目标进化算法已经显示出解决此类问题的出色能力。但是,大多数现有的作品仅使用静态惩罚函数来处理约束,当约束变得严格时,通常会导致过早。另一方面,随着搜索空间变得越来越庞大和混乱,如何在整个进化过程中平衡探索整个搜索空间和开发重要区域的能力变得越来越重要。本文提出了一种基于进化状态的自适应个体评估方案来处理多目标优化问题中的约束。此外,还相应调整了进化参数,以平衡勘探和开发能力。这些与大多数以前的研究不同,后者直接结合了多目标进化算法来为Cloud工作流调度搜索出色的解决方案。实验结果表明,该算法在收敛性和多样性方面均优于其他最新方法,并且在解决云工作流调度问题时也具有更好的优化能力。

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