Browsing by Author "Liu, Kai"
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Item A Hybrid Computing Infrastructure for Climate Simulation(2017) Liu, Kai; Liu, Kai; Yang, ChaoweiThe 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.Item Utilizing Volunteer Computing and Virtualization Technology for Climate Simulation(2014-10-08) Liu, Kai; Liu, Kai; Yang, ChaoweiThe 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 geospatial computing challenges for scientists as follows: 1) enormous computational power is required for running these models and applications to produce results in a reasonable timeframe; 2) climate models are always sensitive and require special computing environments; 3) these models are challenging to provide convenient and fast solution to transfer the big data outputs from climate simulations. Presently, volunteer computing is getting more powerful and provides a potential solution for these problems by obtaining super computational resources from global volunteers. Meanwhile, the virtualization technology based on hardware or platforms allows researchers to run sensitive models in a predefined virtual machine. This thesis reports on research to integrate and optimize volunteer computing and virtualization technology for climate simulation based on the following: 1) utilizing volunteer computing resources so that the heterogeneous home computers can support climate applications; 2) utilizing virtualization technology to make the climate application run on different platforms; 3) optimizing the output collection mechanism to periodically upload climate model output; and 4) optimizing the credit system to grant credits periodically for long time climate simulation tasks. The research is based on NASA and George Mason University’s collaborative project Climate@Home, which is the first volunteer computing project using virtualization technology in the climate domain.