Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Ph. D.) -- Bilkent University, 2012.Includes bibliographical refences.The growth in the amount of data in todays computing problems and the level
of parallelism dictated by the large-scale computing economics necessitates highlevel
parallelism for many applications. This parallelism is generally achieved
via data-parallel solutions that require effective data clustering (partitioning) or
declustering schemes (depending on the application requirements). In addition
to data partitioning/declustering, data replication, which is used for data availability
and increased performance, has also become an inherent feature of many
applications. The data partitioning/declustering and data replication problems
are generally addressed separately. This thesis is centered around the idea of
performing data replication and data partitioning/declustering simultenously to
obtain replicated data distributions that yield better parallelism. To this end,
we utilize query-logs to propose replicated data distribution solutions and extend
the well known Fiduccia-Mattheyses (FM) iterative improvement algorithm
so that it can be used to generate replicated partitioning/declustering of data.
For the replicated declustering problem, we propose a novel replicated declustering
scheme that utilizes query logs to improve the performance of a parallel
database system. We also extend our replicated declustering scheme and propose
a novel replicated re-declustering scheme such that in the face of drastic
query pattern changes or server additions/removals from the parallel database
system, new declustering solutions that require low migration overheads can be
computed. For the replicated partitioning problem, we show how to utilize an
effective single-phase replicated partitioning solution in two well-known applications
(keyword-based search and Twitter). For these applications, we provide the
algorithmic solutions we had to devise for solving the problems that replication
brings, the engineering decisions we made so as to obtain the greatest benefits
from the proposed data distribution, and the implementation details for realistic
systems. Obtained results indicate that utilizing query-logs and performing replication and partitioning/declustering in a single phase improves parallel performance.Türk, AtaPh.D
