Master Thesis - ICS

Abstract
The Grid is an emerging infrastructure that supports the discovery, access and use of
distributed computational resources. Grids abstract over platform or protocol-specific
mechanisms for authentication, file access, data transfer, application invocation, etc. and
allow dynamic deployment of applications on diverse hardware and software platforms.
The scheduling computations and resource management for Grid-aware applications is a
challenging problem as the resources are distributed, heterogeneous in nature, owned by
different individuals or organizations with their own policies, different access and cost
models, and have dynamically varying loads and availability.
A high-performance scheduler in general promotes the performance of individual
applications by optimizing performance measurements such as minimal execution time.
The strategy of efficient and optimized query execution is very challenging research
problem. Besides that, the a-priori resource allocation and management is a hard and
challenging problem, as well. It is extremely important for the researchers and the
distributed database designers to know in advance which 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 in
distributed database systems. In addition, we focus on how we can optimize queries
processing technology with computational Grids and how we can make resource
allocation more efficient and effective. Especially, regarding the case of no data
replication, we designed and implemented a high-performance application scheduler for
relational join queries over a Grid-aware architecture. We transform given join
expressions into directed acyclic graphs (DAGs) that contain all possible plans for the
execution of the join. For that purpose, we developed the Query Plan Graph Constructor
– QuPGC algorithm. When the query plan graph is constructed, we have to select the
execution plan that yields optimal performance. For that reason, we developed the
Heuristic Query Path Selector – HQuPaS algorithm, that uses two heuristic functions for
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