Parallel database systems: the future of high performance database systems
Communications of the ACM
Join and Semijoin Algorithms for a Multiprocessor Database Machine
ACM Transactions on Database Systems (TODS)
Parallelism in relational data base systems: architectural issues and design approaches
DPDS '90 Proceedings of the second international symposium on Databases in parallel and distributed systems
Parallel algorithms for the execution of relational database operations
ACM Transactions on Database Systems (TODS)
Mariposa: A New Architecture for Distributed Data
Proceedings of the Tenth International Conference on Data Engineering
Resource Scheduling for Parallel Query Processing on Computational Grids
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
The design and implementation of Grid database services in OGSA-DAI: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Parallel Processing with the Perfect Shuffle
IEEE Transactions on Computers
Sorting networks and their applications
AFIPS '68 (Spring) Proceedings of the April 30--May 2, 1968, spring joint computer conference
SODA A Distributed Data Management Framework for the Internet of Services
GCC '08 Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing
A Heuristic Query Optimization Approach for Heterogeneous Environments
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
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This paper presents an analytical discussion of algorithms for relational database operations in a grid environment, compares the findings with the classical generalized multiprocessor framework, and describes an optimization algorithm to maximize performance for a heterogeneous environment. We develop a concise but comprehensive analytical model of parallel algorithms for sorting, joining, and aggregation. In our approach we focus on a limited number of characteristic parameters to keep the analytical model clear. It is shown that an expressive model can be built upon just three characteristic parameter sets, namely the node processing performance and the network and the disk bandwidths. These parameters are the input for the optimization process for the orchestration of the execution workflow on the grid. Based on these results the paper proves that using smart enhancement to exploit the heterogeneity of the grid, the performance of the algorithms for database operations can be increased remarkably.