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H2F: A Hierarchical Hadoop Framework for big data processing in geo-distributed environments
No full textA LAHC-based Job Scheduling Strategy to Improve Big Data Processing in Geo-distributed Contexts
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A hierarchical Hadoop framework to handle big data in geo-distributed computing environments
No full textAdvances in the communication technologies, along with the birth of new communication paradigms leveraging on the power of the social, has fostered the production of huge amounts of data. Oldfashioned computing paradigms are unfit to handle the dimensions of the data daily produced by the countless, worldwide distributed sources of information. So far, the MapReduce has been able to keep the promise of speeding up the computation over Big Data within a cluster. This article focuses on scenarios of worldwide distributed Big Data. While stigmatizing the poor performance of the Hadoop framework when deployed in such scenarios, it proposes the definition of a Hierarchical Hadoop Framework (H2F) to cope with the issues arising when Big Data are scattered over geographically distant data centers. The article highlights the novelty introduced by the H2F with respect to other hierarchical approaches. Tests run on a software prototype are also reported to show the increase of performance that H2F is able to achieve in geographical scenarios over a plain Hadoop approach
A Scheduling Strategy to Run Hadoop Jobs on Geodistributed Data
No full textInternet-of-Things scenarios will be typically characterized
by huge amounts of data made available. A challenging task is to efficiently manage such data, by analyzing, elaborating and extracting useful information from them. Distributed computing framework such as Hadoop, based on the MapReduce paradigm, have been used to process such amounts of data by exploiting the computing power of many cluster nodes. However, as long as the computing context is made of clusters of homogeneous nodes interconnected through high speed links, the benefit
brought by the such frameworks is clear and tangible. Unfortunately, in many real big data applications the data to be processed reside in many computationally heterogeneous data centers distributed over the planet. In those contexts, Hadoop was proved to perform very poorly. The proposal presented in this paper addresses this limitation. We designed a context-aware Hadoop framework that is capable of scheduling and distributing tasks among geographically distant clusters in a way that minimizes overall jobs execution time. The proposed scheduler leverages on the integer partitioning technique and on an a-priori knowledge of big data application patterns to explore the space of all possible task schedules and estimate the one expected to perform best. Final experiments conducted on a scheduler prototype prove the benefit of the approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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