A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment
A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment
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DOI：
10.1109/tcc.2015.2451649
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发表时间：
2018
影响因子：
6.5
通讯作者：
Jyoti Sahni;D. P. Vidyarthi
中科院分区：
文献类型：
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作者：
Jyoti Sahni;D. P. Vidyarthi
云计算是一种分布式计算模式，它允许通过互联网交付IT资源，并遵循按需付费的计费模式。工作流调度是云计算中最具挑战性的问题之一。虽然，工作流调度的分布式系统，如网格和集群已被广泛研究，然而，这些解决方案是不可行的云环境。这是因为，云环境与其他分布式环境的不同之处主要体现在两个方面：按需资源配置和按需付费定价模型。因此，为了实现工作流编排到云资源上的真正好处，需要开发可以利用优势并解决特定于云环境的挑战的新方法。本文提出了一种动态的具有成本效益的最后期限约束的启发式算法，用于在公共云中调度科学工作流。所提出的技术的目的是利用云计算提供的优势，同时考虑到虚拟机（VM）的性能变化和实例获取延迟，以确定一个最后期限约束的科学工作流的时间表，以较低的成本。对一些著名科学工作流程的性能评估表明，与当前最先进的启发式算法相比，所提出的算法具有更好的性能。
Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.