Euro-Par 2013: Parallel Processing Workshops: BigDataCloud, by Dieter an Mey, Michael Alexander, Paolo Bientinesi, Mario

By Dieter an Mey, Michael Alexander, Paolo Bientinesi, Mario Cannataro, Carsten Clauss, Alexandru Costan, Gabor Kecskemeti, Christine Morin, Laura Ricci, Julio Sahuquillo, Martin Schulz, Vittorio Scarano, Stephen L. Scott, Josef Weidendorfer (eds.)

This e-book constitutes completely refereed post-conference complaints of the workshops of the nineteenth overseas convention on Parallel Computing, Euro-Par 2013, held in Aachen, Germany in August 2013. The ninety nine papers awarded have been conscientiously reviewed and chosen from one hundred forty five submissions. The papers comprise seven workshops which have been co-located with Euro-Par within the past years: - substantial info Cloud (Second Workshop on substantial info administration in Clouds) - Hetero Par (11th Workshop on Algorithms, versions and instruments for Parallel Computing on Heterogeneous structures) - HiBB (Fourth Workshop on excessive functionality Bioinformatics and Biomedicine) - OMHI (Second Workshop on On-chip reminiscence Hierarchies and Interconnects) - right (Sixth Workshop on productiveness and function) - Resilience (Sixth Workshop on Resiliency in excessive functionality Computing with Clusters, Clouds, and Grids) - UCHPC (Sixth Workshop on Un traditional excessive functionality Computing) in addition to six newbies: - DIHC (First Workshop on Dependability and Interoperability in Heterogeneous Clouds) - Fed ICI (First Workshop on Federative and Interoperable Cloud Infrastructures) - LSDVE (First Workshop on huge Scale dispensed digital Environments on Clouds and P2P) - MHPC (Workshop on Middleware for HPC and large info platforms) -PADABS ( First Workshop on Parallel and allotted Agent dependent Simulations) - ROME (First Workshop on Runtime and working platforms for the various center period) most of these workshops specialise in advertising and development of all facets of parallel and dispensed computing.

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Extra resources for Euro-Par 2013: Parallel Processing Workshops: BigDataCloud, DIHC, FedICI, HeteroPar, HiBB, LSDVE, MHPC, OMHI, PADABS, PROPER, Resilience, ROME, and UCHPC 2013, Aachen, Germany, August 26-27, 2013. Revised Selected Papers

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1 Introduction The emergence of high-performance open-source storage systems is permitting applications and middleware developers to look beyond traditional file-like interfaces towards co-designed, domain-specific storage interfaces that offer unique opportunities for optimization. However, storage interfaces are inextricably tied to the ability to interpret and access data, elevating the criticality of their preservation and management in storage systems to that of the data artifacts themselves.

G. saving energy by powering off disks). 12 B. Nicolae References 1. : MapReduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008) 2. : Hadoop: The Definitive Guide. O’Reilly Media, Inc. (2009) 3. : Cooking with Linux—still searching for the ultimate Linux distro? Linux J. 2007(161), 9 (2007) 4. : The Hadoop distributed file system. In: MSST 2010: The 26th Symposium on Massive Storage Systems and Technologies (2010) 5. : A survey on cloud interoperability: taxonomies, standards, and practice.

C Springer-Verlag Berlin Heidelberg 2014 4 B. Nicolae An important technique to limit the impact of I/O bottlenecks is to avoid data movements as much as possible, which conserves network bandwidth and thus helps achieve horizontal scalability. Several big data paradigms were developed around this concept, with MapReduce [1] and its open-source implementation Hadoop [2] being widely adopted in both academia and industry. Two key design principles enable MapReduce to avoid data movements. First, it forces the users to think their application in an embarrassingly parallel fashion that transforms the input as much as possible into a digested form (map phase) over which an aggregation is performed (reduce phase).

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