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Evaluating Computation and Data Placements in Edge Infrastructures through a Common Simulator

机译:通过通用模拟器评估边缘基础架构中的计算和数据放置

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Scheduling computational jobs with data-sets dependencies is an important challenge of edge computing infrastructures. Although several strategies have been proposed, they have been evaluated through ad-hoc simulator extensions that are, when available, usually not maintained. This is a critical problem because it prevents researchers to –easily– perform fair comparisons between different proposals. In this paper, we propose to address this limitation by presenting a simulation engine dedicated to the evaluation and comparison of scheduling and data movement policies for edge computing use-cases. Built upon the Batsim/SimGrid toolkit, our tool includes an injector that allows the simulator to replay a series of events captured in real infrastructures. It also includes a controller that supervises storage entities and data transfers during the simulation, and a plug-in system that allows researchers to add new models to cope with the diversity of edge computing devices. We demonstrate the relevance of such a simulation toolkit by studying two scheduling strategies with four data movement policies on top of a simulated version of the Qarnot Computing platform, a production edge infrastructure based on smart heaters. We chose this use-case as it illustrates the heterogeneity as well as the uncertainties of edge infrastructures. Our ultimate goal is to gather industry and academics around a common simulator so that efforts made by one group can be factorised by others.
机译:计划具有数据集相关性的计算作业是边缘计算基础架构的一项重要挑战。尽管已经提出了几种策略,但是它们已经通过临时模拟器扩展进行了评估,这些扩展通常在可用时不予维护。这是一个关键问题,因为它阻止研究人员轻松地在不同建议之间进行公平的比较。在本文中,我们建议通过提供一个专用于边缘计算用例的调度和数据移动策略的评估和比较的仿真引擎来解决这一限制。我们的工具以Batsim / SimGrid工具包为基础构建,包括一个注入器,该注入器允许模拟器重播在实际基础架构中捕获的一系列事件。它还包括一个控制器,该控制器在仿真过程中监督存储实体和数据传输,以及一个插件系统,允许研究人员添加新模型来应对边缘计算设备的多样性。通过在Qarnot Computing平台(基于智能加热器的生产边缘基础设施)的仿真版本上研究两种调度策略和四种数据移动策略,我们证明了这种仿真工具包的相关性。我们选择此用例是因为它说明了边缘基础结构的异质性和不确定性。我们的最终目标是围绕一个共同的模拟器聚集行业和学者,以便一个团队的工作可以被其他团队考虑。

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