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A Hybrid Computing Infrastructure for Climate Simulation

机译：用于气候模拟的混合计算基础架构

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摘要

The climatological community relies increasingly on computing intensive models and applications to study atmospheric chemistry, aerosols, carbon cycle and other tracer gases. These models and applications are becoming increasingly complex and bring computing challenges including: 1) the enormous computational power required for running these models and applications to produce results in a reasonable timeframe; 2) the challenging in providing convenient and fast solutions distributing and storing the massive climate model outputs; 3) the lack of methods for visualizing the climate simulation results efficiently and reliably. Volunteer computing provides a potential solution for tackling the computational power problem by obtaining large amounts of computational resources from global volunteers. Meanwhile, virtualization technology allows researchers to run climate models in a predefined virtual machine. Cloud computing storage provides advantages for distributing and storing climate data and outputs with low-cost. The Load Balancer and Auto Scaling in cloud computing provides a good solution to visualize the climate simulation results. This dissertation reports our research on integrating and optimizing volunteer computing, virtualization technology and cloud computing for climate simulation by: 1) using volunteer computing resources to leverage large number of home computers to support climate simulations; using virtualization technology to enable the climate models run on heterogeneous computers while providing bit-level homogeneous computing environment; optimizing the output collection mechanism to periodically upload climate model output; and optimizing the credit system to grant credits periodically to volunteers for volunteer retention to support long time climate simulation tasks. 2) Using cloud Simple Storage Service provided by leading cloud providers to develop a global replication storage to distribute cloud models and data to global volunteers. 3) Using Load Balancing to distribute incoming WMS requests across multiple cloud instances to improve the performance; Using Auto Scaling to help to maintain climate visualization availability and allows climate scientists to dynamically scale the cloud capacity. A prototype is developed to demonstrate the feasibility and efficiency of proposed techniques. The prototype is further tested in the Climate Home project, a hybrid computing project using volunteer computing and cloud computing. Result shows that this research provides a computationally efficient and usable approach to accelerate climate simulation.
机译：气候界越来越依赖计算密集型模型和应用程序来研究大气化学，气溶胶，碳循环和其他示踪气体。这些模型和应用程序变得越来越复杂，并带来了计算挑战，其中包括：1）运行这些模型和应用程序以在合理的时间内产生结果所需的巨大计算能力； 2）提供方便，快速的解决方案来分发和存储大量气候模型输出结果具有挑战性； 3）缺乏有效，可靠地可视化气候模拟结果的方法。志愿者计算通过从全球志愿者那里获取大量计算资源，为解决计算能力问题提供了一种潜在的解决方案。同时，虚拟化技术使研究人员可以在预定义的虚拟机中运行气候模型。云计算存储为以低成本分发和存储气候数据和输出提供了优势。云计算中的负载均衡器和Auto Scaling为可视化气候模拟结果提供了一个很好的解决方案。本论文通过以下方法报告了我们在整合和优化气候计算中的志愿者计算，虚拟化技术和云计算方面的研究：1）使用志愿者计算资源来利用大量家用计算机来支持气候模拟；使用虚拟化技术使气候模型能够在异构计算机上运行，​​同时提供比特级的同类计算环境；优化输出收集机制，定期上传气候模型输出；优化信贷制度，定期向志愿者授予信贷，以留住志愿者，以支持长期的气候模拟任务。 2）使用领先的云提供商提供的云简单存储服务来开发全局复制存储，以将云模型和数据分发给全球志愿者。 3）使用负载平衡在多个云实例之间分配传入的WMS请求，以提高性能；使用Auto Scaling帮助维护气候可视化可用性，并使气候科学家能够动态扩展云容量。开发了一个原型来证明所提出技术的可行性和效率。该原型在气候家庭项目中进行了进一步测试，该项目是使用志愿者计算和云计算的混合计算项目。结果表明，该研究提供了一种计算有效且可用的方法来加速气候模拟。

著录项

  • 作者

    Liu, Kai.;

  • 作者单位

    George Mason University.;

  • 授予单位 George Mason University.;
  • 学科 Geographic information science and geodesy.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 83 p.
  • 总页数 83
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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