4,505 research outputs found
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
ElasTraS: An Elastic Transactional Data Store in the Cloud
Over the last couple of years, "Cloud Computing" or "Elastic Computing" has
emerged as a compelling and successful paradigm for internet scale computing.
One of the major contributing factors to this success is the elasticity of
resources. In spite of the elasticity provided by the infrastructure and the
scalable design of the applications, the elephant (or the underlying database),
which drives most of these web-based applications, is not very elastic and
scalable, and hence limits scalability. In this paper, we propose ElasTraS
which addresses this issue of scalability and elasticity of the data store in a
cloud computing environment to leverage from the elastic nature of the
underlying infrastructure, while providing scalable transactional data access.
This paper aims at providing the design of a system in progress, highlighting
the major design choices, analyzing the different guarantees provided by the
system, and identifying several important challenges for the research community
striving for computing in the cloud.Comment: 5 Pages, In Proc. of USENIX HotCloud 200
TAPER: query-aware, partition-enhancement for large, heterogenous, graphs
Graph partitioning has long been seen as a viable approach to address Graph
DBMS scalability. A partitioning, however, may introduce extra query processing
latency unless it is sensitive to a specific query workload, and optimised to
minimise inter-partition traversals for that workload. Additionally, it should
also be possible to incrementally adjust the partitioning in reaction to
changes in the graph topology, the query workload, or both. Because of their
complexity, current partitioning algorithms fall short of one or both of these
requirements, as they are designed for offline use and as one-off operations.
The TAPER system aims to address both requirements, whilst leveraging existing
partitioning algorithms. TAPER takes any given initial partitioning as a
starting point, and iteratively adjusts it by swapping chosen vertices across
partitions, heuristically reducing the probability of inter-partition
traversals for a given pattern matching queries workload. Iterations are
inexpensive thanks to time and space optimisations in the underlying support
data structures. We evaluate TAPER on two different large test graphs and over
realistic query workloads. Our results indicate that, given a hash-based
partitioning, TAPER reduces the number of inter-partition traversals by around
80%; given an unweighted METIS partitioning, by around 30%. These reductions
are achieved within 8 iterations and with the additional advantage of being
workload-aware and usable online.Comment: 12 pages, 11 figures, unpublishe
Supporting Complex Scientific Database Schemas in a Grid Middleware
“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/AINA.2009.129The volume of digital scientific data has increased considerably with advancing technologies of computing devices and scientific instruments. We are exploring the use of emerging Grid technologies for the management and manipulation of very large distributed scientific datasets. Taking as an example a terabyte-size scientific database with complex database schema, this paper focuses on the potential of a well-known Grid middleware - OGSA-DQP - for distributing such datasets. In particular, we investigate and extend the data type support in this system to handle a complex schema of a real scientific database - the Sloan Digital Sky Survey database
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