Master Thesis - ICS

Computer Science Department
Antonis Misargopoulos
for the completion of a specific execution. In addition, the scheduling technique we
propose is quite flexible, and newly proposed, optimized join execution algorithms can be
considered. Note that for the evaluation of our scheduler, we designed and implement a
scheduling simulator that takes as input an initial multi-join query and gives the bestperformance
execution plan and the time that is needed for its completion.
In contrast to dynamic programming, our approach has an important advantage:
the relations involved in the join queries are physically stored at distributed remote
resources over a Grid. The cost model we use takes advantage of Grid characteristics;
such as the number of resources, the bandwidth of the links between them, etc.
Specifically, the heuristics we use, comprise both the computation and the
communication cost for the execution of a given query.
Another advantage over the dynamic programming approach is the ability for
parallel execution that is provided by the Grid facilities. It is extremely powerful because,
on the Grid, we are able to separate our tasks into fragments that can be executed in
parallel, saving cost in time and space. As we are going to describe in detail later, the
plan graph that is constructed for each given join query using the QuPGC algorithm can
be easily fragmented and executed in parallel, reducing the cost in time for the graph
construction dramatically. Furthermore, using HQuPaS for the selection of the optimal
query plan, the heuristics of the cost model can also be calculated in parallel. In the
following sections, we describe our scheduling approach, our algorithms and our
simulator in detail.
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