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A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments

机译：多云环境下大数据应用的预处理工作流调度方法

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摘要

The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle big data problems. One of the biggest challenges in multiclouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for multicloud environments. Moreover, the existing research works either fail to satisfy the quality of service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm, which is called multiclouds partial critical paths with pretreatment (MCPCPP), for big data workflows in multiclouds is presented. This algorithm incorporates the concept of partial critical paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into consideration the essential characteristics of multiclouds such as the charge per time interval, various instance types from different cloud providers, as well as homogeneous intrabandwidth vs. heterogeneous interbandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.
机译：最新的分布式计算范例（即云计算）的快速发展产生了由众多云提供商组成的高度分散的云市场，并提供了巨大的并行计算能力来处理大数据问题。多云中最大的挑战之一是有效的工作流调度。尽管已经对工作流调度问题进行了广泛研究，但针对多云环境量身定制的主要工作仍然很少。此外，现有的研究工作要么不能满足服务质量（QoS）要求，要么没有考虑云计算的一些基本特征，例如计算资源的异构性和弹性。本文提出了一种针对多云中的大数据工作流的调度算法，该算法称为带有预处理的多云局部关键路径（MCPCPP）。该算法结合了部分关键路径的概念，旨在在满足定义的截止期限约束的同时最大程度地减少工作流的执行成本。我们的方法考虑了多云的基本特征，例如每时间间隔的收费，来自不同云提供商的各种实例类型以及同质的内部带宽与异类的内部带宽。各种类型的工作流用于评估目的，我们的实验结果表明MCPCPP是有前途的。

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