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Titlebook: Energy Efficiency in Large Scale Distributed Systems; COST IC0804 European Jean-Marc Pierson,Georges Da Costa,Lars Dittmann Conference proc

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https://doi.org/10.1057/9781137390813plex applications such as collaborative analysis, information sharing, and data mining in a mobile context. A key aspect to be addressed to enable effective and reliable data mining over mobile devices is ensuring energy efficiency. In particular, energy characterization plays a critical role in det
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https://doi.org/10.1057/9780230593527ild compute infrastructures from virtualized resources. Particularly, once installed and configured it allows its users to submit and control the life-cycle of a large number of VMs. For scalability, the system relies on a self-organizing and healing hierarchical architecture. Moreover, it performs
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https://doi.org/10.1007/978-981-16-0757-8 estimate their performance, energy consumption, and energy-efficiency metrics for diverse workloads and management policies. We present its functionality in terms of energy-efficiency modeling and methods of power usage estimations available in the simulator. Moreover, we show an application of DCw
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https://doi.org/10.1007/978-981-19-3862-7roposed static solutions to prove the integrity of a file. For the dynamic case, Rank-Based Authenticated Skip list (RBASL) has been presented. It provides the update operations with logarithmic complexity. However, an RBASL expects the block size to be fixed. In a realistic scenario, where the upda
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https://doi.org/10.1057/9781137306814he use of low power processors such as Intel Atom and ARM Cortex have recently gained more interest. In this article, we compare performance and energy efficiency of cutting-edge high-density HPC platform enclosures featuring either very high-performing processors (such as Intel Core i7 or E7) yet h
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978-3-642-40516-7Springer-Verlag Berlin Heidelberg 2013
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Energy Efficiency in Large Scale Distributed Systems978-3-642-40517-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
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