Master Thesis - ICS

Computer Science Department
Antonis Misargopoulos
For years now, research has been done on the design, implementation and performance of
parallel distributed database systems. Teradata [94], Budda [95], HC186-16 [96],
GAMMA [97], and XPRS [98] are examples of systems that actually were implemented.
The performance evaluation of these systems though is mainly limited to simple queries
that involve no more than one or two join operations. Recent developments in the
direction of support of nonstandard applications, the use of complex data models, and the
availability of high-level interfaces tend to generate complex queries that may contain
larger numbers of joins between relations. Consequently, the development of execution
strategies for the parallel evaluation of multi-join queries has drawn the attention of the
scientific community. A high-performance scheduler in general promotes the
performance of individual applications by optimizing performance measurements such as
minimal execution time or speed-up. The strategy of efficient and optimized query
execution that is adopted is very challenging research problem.
One central component of a query optimizer is its search strategy or enumeration
algorithm. The enumeration algorithm of the optimizer determines which plans to
enumerate, and the classic enumeration algorithm is based on Dynamic Programming
proposed in IBM’s System R project [145].
Besides that, the a-priori resource allocation and management is a hard-solved and
challenging problem, as well. It is extremely important for the researchers and the
distributed database designers to be aware of what and how many resources of the Grid
architecture are involved in the execution of a given query. Communication and
computation costs are two very important factors that designate the proper computation
resources for this execution.
This work explores this aspect of service-based computing and resource
management. We study the various data replication policies that could be followed and
focus on how we can optimize queries processing technology with service-based Grids
and how we can make resource allocation or co-allocation more efficient and effective.
Especially, regarding the case of no data replication, we designed and implemented a
high-performance dynamic application scheduler for relational linear join queries over a
Grid architecture. According to the characteristics of the resources of the Grid, the
scheduler takes an initial join query and designates the proper query plan execution for
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