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Federating Advanced Cyberinfrastructures with Autonomic Capabilities

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Cloud Computing for Data-Intensive Applications

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Abstract

Cloud computing has emerged as a dominant paradigm that has been widely adopted by enterprises. Clouds provide on-demand access to computing utilities, an abstraction of unlimited computing resources, and support for on-demand scale up, scale down and scale out. Clouds are also rapidly joining high performance computing system, clusters and grids as viable platforms for scientific exploration and discovery. Furthermore, dynamically federated Cloud-of-Clouds infrastructure can support heterogeneous and highly dynamic applications requirements by composing appropriate (public and/or private) cloud services and capabilities. As a result, providing scalable and robust mechanisms to federate distributed infrastructures and handle application workflows, that can effectively utilize them, is critical. In this chapter, we present a federation model to support the dynamic federation of resources and autonomic management mechanisms that coordinate multiple workflows to use resources based on objectives. We demonstrate the effectiveness of the proposed framework and autonomic mechanisms through the discussion of an experimental evaluation of illustrative use case application scenarios, and from these experiences, we discuss that such a federation model can support new types of application formulations.

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Acknowledgements

The research presented in this work is supported in part by US National Science Foundation (NSF) via grants numbers OCI 1310283, OCI 1339036, DMS 1228203 and IIP 0758566, by the Director, Office of Advanced Scientific Computing Research, Office of Science, of the U.S. Department of Energy through the Scientific Discovery through Advanced Computing (SciDAC) Institute of Scalable Data Management, Analysis and Visualization (SDAV) under award number DE-SC0007455, the Advanced Scientific Computing Research and Fusion Energy Sciences Partnership for Edge Physics Simulations (EPSI) under award number DE-FG02-06ER54857, the ExaCT Combustion Co-Design Center via subcontract number 4000110839 from UT Battelle, and by an IBM Faculty Award. We used resources provided by: XSEDE NSF OCI-1053575, FutureGrid NSF OCI-0910812, and NERSC Center DOE DE-AC02-05CH11231. The research and was conducted as part of the NSF Cloud and Autonomic Computing (CAC) Center at Rutgers University and the Rutgers Discovery Informatics Institute (RDI2). We would also like to acknowledge Hyunjoo Kim, Moustafa AbdelBaky, and Aditya Devarakonda for their contributions to the CometCloud project.

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  1. Rutgers Discovery Informatics Institute, NSF Cloud and Autonomic Computing Center, Department of Electrical and Computer Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ, 08854, USA

    Javier Diaz-Montes, Ivan Rodero, Mengsong Zou & Manish Parashar

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  1. Javier Diaz-Montes
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    Xiaolin Li

  2. Indiana University, Bloomington, Indiana, USA

    Judy Qiu

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Diaz-Montes, J., Rodero, I., Zou, M., Parashar, M. (2014). Federating Advanced Cyberinfrastructures with Autonomic Capabilities. In: Li, X., Qiu, J. (eds) Cloud Computing for Data-Intensive Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1905-5_9

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