Magellan Final Report - Office of Science - U.S. Department of Energy

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Magellan Final Report • The cloud model fundamentally changes the user support that is necessary and available to end users. A description of the user support model at the two sites is provided in Chapter 7. • Understanding the security technologies and policies possible in cloud environments is key to evaluating the feasibility of cloud computing for the DOE Office of Science. We outline our efforts in the project and identify the challenges and gaps in current day technologies in Chapter 8. • Our benchmarking of virtualized cloud environments and workload analysis is detailed in Chapter 9. • The use of MapReduce for scientific applications and our evaluations is discussed in Chapter 10. • Case studies of applications are discussed, and the gaps and challenges of using cloud environments from a user perspective are identified in Chapter 11. • Finally, our cost analysis is discussed in Chapter 12. 14
Chapter 4 Application Characteristics A challenge with exploiting cloud computing for science is that the scientific community has needs that can be significantly different from typical enterprise customers. Applications often run in a tightly coupled manner at scales greater than most enterprise applications require. This often leads to bandwidth and latency requirements that are more demanding than most cloud customers. Scientific applications also typically require access to large amounts of data including large volumes of legacy data. This can lead to large startup cost and data storage cost. The goal of Magellan has been to analyze these requirements using an advanced testbed, a flexible software stack, and performing a range of data gathering efforts. In this chapter we summarize some of the key application characteristics that are necessary to understand the feasibility of clouds for these scientific applications. In Section 4.1 we summarize the computational models, and in Section 4.2 we discuss some diverse usage scenarios. We summarize the results of the user survey in Section 4.3 and discuss some scientific use cases in Section 4.4. 4.1 Computational Models Scientific workloads can be classified into three broad categories based on their resource requirements: largescale tightly coupled computations, mid-range computing, and high throughput computing. In this section, we provide a high-level classification of workloads in the scientific space, based on their resource requirements and delve into the details of why cloud computing is attractive to these application spaces. Large-Scale Tightly Coupled. These are complex scientific codes generally running at large-scale supercomputing centers across the nation. Typically, these are MPI codes using a large number of processors (often in the order of thousands). A single job can use thousands to millions of core hours depending on the scale. These jobs are usually run at supercomputing centers through batch queue systems. Users wait in a managed queue to access the resources requested, and their jobs are run when the required resources are available and no other jobs are ahead of them in the priority list. Most supercomputing centers provide archival storage and parallel file system access for the storage and I/O needs of these applications. Applications in this class are expected to take a performance hit when working in virtualized cloud environments [46]. Mid-Range Tightly Coupled. These applications run at a smaller scale than the large-scale jobs. There are a number of codes that need tens to hundreds of processors. Some of these applications run at supercomputing centers and backfill the queues. More commonly, users rely on small compute clusters that are managed by the scientific groups themselves to satisfy these needs. These mid-range applications are good candidates for cloud computing even though they might incur some performance hit. High Throughput. Some scientific explorations are performed on the desktop or local clusters and have asynchronous, independent computations. Even in the case of large-scale science problems, a number of the 15
Page 1 and 2: The Magellan Report on Cloud Comput
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Chapter 13 Conclusions Cloud comput
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Bibliography [1] G. Aldering, G. Ad
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Magellan Final Report [30] I. Foste
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Magellan Final Report [67] M. Palan
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Appendix A Publications Selected Pr
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Appendix B Surveys B1
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• Nuclear Physics - Accelarator P
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Allow users to edit responses. What
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Amazon Eucalyptus OpenStack Other:
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Please list any publications/report
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Hadoop Streaming Hadoop Native Prog
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Magellan Final Report - Office of Science - U.S. Department of Energy

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