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首页> 外文期刊>Journal of supercomputing >CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud
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CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud

机译：CCA：用于云上多核资源的受期限限制的工作流调度算法

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摘要

Workflows are adopted as a powerful modeling technique to represent diverse applications in different scientific fields as a number of loosely coupled tasks. Given the unique features of cloud technology, the issue of cloud workflow scheduling is a critical research topic. Users can utilize services on the cloud in a pay-as-you-go manner and meet their quality of service (QoS) requirements. In the context of the commercial cloud, execution time and especially execution expenses are considered as two of the most important QoS requirements. On the other hand, the remarkable growth of multicore processor technology has led to the use of these processors by Infrastructure as a Service cloud service providers. Therefore, considering the multicore processing resources on the cloud, in addition to time and cost constraints, makes cloud workflow scheduling even more challenging. In this research, a heuristic workflow scheduling algorithm is proposed that attempts to minimize the execution cost considering a user-defined deadline constraint. The proposed algorithm divides the workflow into a number of clusters and then an extendable and flexible scoring approach chooses the best cluster combinations to achieve the algorithm's goals. Experimental results demonstrate a great reduction in resource leasing costs while the workflow deadline is met.
机译：工作流被用作一种强大的建模技术，以松散耦合的任务的形式代表着不同科学领域的各种应用。鉴于云技术的独特功能，云工作流调度问题是一个至关重要的研究主题。用户可以按需付费的方式使用云上的服务，并满足他们的服务质量（QoS）要求。在商业云的背景下，执行时间，尤其是执行费用被视为最重要的QoS要求中的两个。另一方面，多核处理器技术的飞速发展已导致基础架构即服务云服务提供商使用了这些处理器。因此，除了时间和成本约束之外，考虑云上的多核处理资源还会使云工作流调度更具挑战性。在这项研究中，提出了一种启发式工作流调度算法，该算法尝试考虑用户定义的期限约束来最小化执行成本。提出的算法将工作流划分为多个聚类，然后采用可扩展且灵活的评分方法选择最佳聚类组合以实现算法的目标。实验结果表明，在满足工作流程期限的情况下，资源租赁成本大大降低。

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