The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms.

中文翻译：

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一种基于改进模糊自适应遗传算法的IaaS云工作流调度方法

具有大量资源按需提供的云平台的出现促使研究人员将科学工作流程迁移到云环境。具有不同 QoS 参数的工作流的调度不是一项微不足道的任务，而是一个 NP 完全问题。已经研究了 QoS 约束工作流的几种启发式方法。然而，他们中的大多数只关注时间和成本，并不能保证高资源利用率。在定义的时间限制内在最少的云资源上调度工作流任务是一个严重的问题。在本文中，制定了一种名为 MFGA（改进的模糊自适应遗传算法）的算法，以在截止日期和预算约束下最小化完工时间并提高资源利用率。还设计了一个模糊逻辑控制器来控制交叉和变异率，以防止 MFGA 陷入局部最优。MFGA 有一种新颖的交叉技术，可以在种群中添加最适合的解决方案。此外，还引入了一种新的变异技术，它可以最大限度地减少 makespan 并提高资源的可重用性。真实工作流程的仿真实验表明，所提出的 MFGA 优于其他最先进的算法。