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International Conference on Database and Expert Systems Applications

DEXA 2013: Database and Expert Systems Applications pp 224–238Cite as

Exploring Data Locality for Clustered Enterprise Applications

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8055))

Abstract

Exploring data locality is crucial to achieve good performance on a distributed system. For many complex, constantly evolving applications, relying on programmers to write their code so as to explore data locality results often in sub-par performance. We propose an automatic approach for dealing with this problem. Instead of expecting programmers to identify data locality, the solution developed here relies on a stochastic analysis of the data-access patterns exhibited by the application at run-time. The analysis makes it possible to correlate not only domain data but application functionality as well. This information is used to explore data locality in clustered enterprise applications by combining two orthogonal and complementary approaches. The first approach reduces the memory foot-print by using a more compact in-memory representation for the application’s domain classes and, furthermore, by delaying the loading of less frequently accessed data. The second approach generates a new request distribution policy. It employs the Latent Dirichlet Allocation partitioning algorithm, generating sub-sets of highly correlated application functionality. Every cluster node is responsible for processing requests belonging to a single sub-set. The combination of these approaches allows cluster nodes to make better use of their memory, thereby increasing the computational efficiency of the system. The work has been validated on the TPC-W benchmark, demonstrating significant performance improvements.

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Author information

Authors and Affiliations

  1. INESC-ID Lisboa / Instituto Superior Técnico, Universidade Técnica de Lisboa, Rua Alves Redol, 9, 1000-029, Lisboa, Portugal

    Stoyan Garbatov & João Cachopo

Authors
  1. Stoyan Garbatov
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  2. João Cachopo
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Editor information

Editors and Affiliations

  1. Instituto Tecnológico de Informática, Valencia, Spain

    Hendrik Decker

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, 166 27, Prague 6, Czech Republic

    Lenka Lhotská

  3. Department of Computer Science, The University of Auckland, 1010, Auckland, New Zealand

    Sebastian Link

  4. Department of Information Technologies, University of Economics, Winston Churchill Square 4, 130 67, Prague 3, Czech Republic

    Josef Basl

  5. Institute of Software Technology, Vienna University of Technology, Favoritenstraße 9-11 / 188, 1040, Vienna, Austria

    A Min Tjoa

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© 2013 Springer-Verlag Berlin Heidelberg

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Garbatov, S., Cachopo, J. (2013). Exploring Data Locality for Clustered Enterprise Applications. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_20

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  • DOI: https://doi.org/10.1007/978-3-642-40285-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40284-5

  • Online ISBN: 978-3-642-40285-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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