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Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication

机译:通过任务复制满足公共云中科学工作流程的最后期限

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The elasticity of Cloud infrastructures makes them a suitable platform for execution of deadline-constrained workflow applications, because resources available to the application can be dynamically increased to enable application speedup. Existing research in execution of scientific workflows in Clouds either try to minimize the workflow execution time ignoring deadlines and budgets or focus on the minimization of cost while trying to meet the application deadline. However, they implement limited contingency strategies to correct delays caused by underestimation of tasks execution time or fluctuations in the delivered performance of leased public Cloud resources. To mitigate effects of performance variation of resources on soft deadlines of workflow applications, we propose an algorithm that uses idle time of provisioned resources and budget surplus to replicate tasks. Simulation experiments with four well-known scientific workflows show that the proposed algorithm increases the likelihood of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.
机译:云基础架构的弹性使其成为执行受期限限制的工作流应用程序的合适平台,因为可以动态增加应用程序可用的资源以实现应用程序加速。在Clouds中执行科学工作流的现有研究要么尝试在不考虑截止日期和预算的情况下最小化工作流执行时间,要么在试图满足应用程序截止时间的同时着重于最小化成本。但是,他们实施了有限的应急策略,以纠正由于低估了任务执行时间或租用公共云资源的交付性能波动而导致的延迟。为了减轻资源性能变化对工作流应用程序的软截止期限的影响,我们提出了一种算法,该算法使用已调配资源的空闲时间和预算盈余来复制任务。用四个众所周知的科学工作流程进行的仿真实验表明,随着可用于复制的预算的增加,所提出的算法增加了满足期限的可能性,并减少了应用程序的总执行时间。

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