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A hybrid genetic algorithm for scientific workflow scheduling in cloud environment

机译：一种云环境中科学工作流程调度的混合遗传算法

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摘要

Nowadays, we live an unprecedented evolution in cloud computing technology that coincides with the development of the vast amount of complex interdependent data which make up the scientific workflows. All these circumstances developments have made the issue of workflow scheduling very important and of absolute priority to all overlapping parties as the provider and customer. For that, work must be focused on finding the best strategy for allocating workflow tasks to available computing resources. In this paper, we consider the scientific workflow scheduling in cloud computing. The main role of our model is to optimize the time needed to run a set of interdependent tasks in cloud and in turn reduces the computational cost while meeting deadline and budget. To this end, we offer a hybrid approach based on genetic algorithm for modelling and optimizing a workflow-scheduling problem in cloud computing. The heterogeneous earliest finish time (HEFT), an heuristic model, intervenes in the generation of the initial population. Based on results obtained from our simulations using real-world scientific workflow datasets, we demonstrate that the proposed approach outperforms existing HEFT and other strategies examined in this paper. In other words, experiments show high efficiency of our proposed approach, which makes it potentially applicable for cloud workflow scheduling. For this, we develop a GA-based module that was integrated to the WorkflowSim framework based on CloudSim.
机译：如今，我们在云计算技术中实现了一个前所未有的演变，恰逢发展了构成科学工作流的大量复杂相互依存数据。所有这些情况的发展都使工作流程调度非常重要，并且绝对优先考虑所有重叠缔约方作为提供者和客户。为此，必须侧重于找到为可用计算资源分配工作流任务的最佳策略。在本文中，我们考虑云计算中的科学工作流程调度。我们的模型的主要作用是优化运行云中一组相互依存任务所需的时间，而且在满足截止日期和预算时降低了计算成本。为此，我们提供了一种基于遗传算法的混合方法，用于在云计算中建模和优化工作流程调度问题。异构最早的结束时间（HEFT），启发式模型，干预初始群体。基于使用现实世界科学工作流数据集从我们的模拟获得的结果，我们证明了所提出的方法优于本文中审查的现有的HEFT和其他策略。换句话说，实验表明了我们所提出的方法的高效率，这使得可能适用于云工作流程调度。为此，我们开发了一个基于GA的模块，该模块集成到基于CloudSim的Workflowsim框架。

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