1,610 research outputs found
Design Architecture-Based on Web Server and Application Cluster in Cloud Environment
Cloud has been a computational and storage solution for many data centric
organizations. The problem today those organizations are facing from the cloud
is in data searching in an efficient manner. A framework is required to
distribute the work of searching and fetching from thousands of computers. The
data in HDFS is scattered and needs lots of time to retrieve. The major idea is
to design a web server in the map phase using the jetty web server which will
give a fast and efficient way of searching data in MapReduce paradigm. For real
time processing on Hadoop, a searchable mechanism is implemented in HDFS by
creating a multilevel index in web server with multi-level index keys. The web
server uses to handle traffic throughput. By web clustering technology we can
improve the application performance. To keep the work down, the load balancer
should automatically be able to distribute load to the newly added nodes in the
server
H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution
The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we addressthe challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.Peer ReviewedPostprint (author's final draft
Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for Scalable Distributed Machine Learning Algorithms
The implementation of a vast majority of machine learning (ML) algorithms
boils down to solving a numerical optimization problem. In this context,
Stochastic Gradient Descent (SGD) methods have long proven to provide good
results, both in terms of convergence and accuracy. Recently, several
parallelization approaches have been proposed in order to scale SGD to solve
very large ML problems. At their core, most of these approaches are following a
map-reduce scheme. This paper presents a novel parallel updating algorithm for
SGD, which utilizes the asynchronous single-sided communication paradigm.
Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent
(ASGD) provides faster (or at least equal) convergence, close to linear scaling
and stable accuracy
Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML
OpenAIRE, the Open Access Infrastructure for Research in Europe, comprises a
database of all EC FP7 and H2020 funded research projects, including metadata
of their results (publications and datasets). These data are stored in an HBase
NoSQL database, post-processed, and exposed as HTML for human consumption, and
as XML through a web service interface. As an intermediate format to facilitate
statistical computations, CSV is generated internally. To interlink the
OpenAIRE data with related data on the Web, we aim at exporting them as Linked
Open Data (LOD). The LOD export is required to integrate into the overall data
processing workflow, where derived data are regenerated from the base data
every day. We thus faced the challenge of identifying the best-performing
conversion approach.We evaluated the performances of creating LOD by a
MapReduce job on top of HBase, by mapping the intermediate CSV files, and by
mapping the XML output.Comment: Accepted in 0th Metadata and Semantics Research Conferenc
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