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

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Magellan Final Report challenges while using cloud resources. This close work with our user groups helped us identify some key gaps in current cloud technologies for science. 1.5 Outline of Report The rest of this report is organized as follows. Chapter 2 describes the cloud service models and discusses application models. Chapter 3 provides a timeline and overview of Magellan project activities. Chapter 4 summarizes the identified requirements or use cases for clouds from discussions with DOE user groups. We describe the Magellan testbed in Chapter 5. We compare and contrast the features and our experiences of various popular virtualized software stack offerings in Chapter 6. We discuss user support issues in Chapter 7 and detail our security analysis in Chapter 8. Our benchmarking efforts are described in Chapter 9. We detail our experiences with the MapReduce programming and specifically the open-source Apache Hadoop implementation in Chapter 10. We present the case studies of some key applications on Magellan and summarize the challenges of using cloud environments in Chapter 11. Our cost analysis is presented in Chapter 12. We present our conclusions in Chapters 13. 4
Chapter 2 Background The term “cloud computing” covers a range of delivery and service models. The common characteristic of these service models is an emphasis on pay-as-you-go and elasticity, the ability to quickly expand and collapse the utilized service as demand requires. Thus new approaches to distributed computing and data analysis have also emerged in conjunction with the growth of cloud computing. These include models like MapReduce and scalable key-value stores like Big Table [11]. Cloud computing technologies and service models are attractive to scientific computing users due to the ability to get on-demand access to resources to replace or supplement existing systems, as well as the ability to control the software environment. Scientific computing users and resource providers servicing these users are considering the impact of these new models and technologies. In this section, we briefly describe the cloud service models and technologies to provide some foundation for the discussion. 2.1 Service Models Cloud offerings are typically categorized as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Each of these models can play a role in scientific computing. The distinction between the service models is based on the layer at which the service is abstracted to the end user (e.g., hardware, system software, etc.). The end user then has complete control over the software stack above the abstracted level. Thus, in IaaS, a virtual machine or hardware is provided to the end user and the user then controls the operating system and the entire software stack. We describe each of these service models and visit existing examples in the commercial cloud space to understand their characteristics. 2.1.1 Infrastructure as a Service In the Infrastructure as a Service provisioning model, an organization outsources equipment including storage, hardware, servers, and networking components. The service provider owns the equipment and is responsible for housing, running, and maintaining it. In the commercial space, the client typically pays on a per-use basis for use of the equipment. Amazon Web Services is the most widely used IaaS cloud computing platform today. Amazon provides a number of different levels of computational power for different pricing. The primary methods for data storage in Amazon EC2 are S3 and Elastic Block Storage (EBS). S3 is a highly scalable key-based storage system that transparently handles fault tolerance and data integrity. EBS provides a virtual storage device that can be associated with an elastic computing instance. S3 charges for space used per month, the volume of data transferred, and the number of metadata operations (in allotments of 1000). EBS charges for data stored per month. For both S3 and EBS, there is no charge for data transferred to and from EC2 within a domain (e.g., the U.S. or Europe). 5
Page 1 and 2: The Magellan Report on Cloud Comput
Page 3 and 4: Executive Summary The goal of Magel
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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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Magellan Final Report Magellan Rese
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Magellan Final Report Selected Mage
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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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