Dynamic Load Balancing in Very Large Shared-Nothing Hypercube Database Computers
IEEE Transactions on Computers
ACM SIGMOD Record
Programming and Computing Software
The NUMA with Clusters of Processors for Parallel Join
IEEE Transactions on Knowledge and Data Engineering
Optimizing Large Join Queries Using A Graph-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Survey of Architectures of Parallel Database Systems
Programming and Computing Software
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The most debated architectures for parallel database processing are Shared Nothing (SN) and Shared Everything (SE) structures. Although SN is considered to be most scalable, it is very sensitive to the data skew problem. On the other hand, SE allows the collaborating processors to share the work load more efficiently. It, however, suffers from the limitation of the memory and disk I/O bandwidth.In this paper, we present a hybrid architecture in which SE clusters are interconnected through a communication network to form a SN structure at the inter-cluster level. In this approach, processing elements are clustered into SE systems to minimize the skew effect. Each cluster, however, is kept small within the limitation of the memory and I/O technology to avoid the data access bottleneck.A generalized performance model was developed to perform sensitivity analysis for the hybrid structure, and to compare it against SE and SN organizations. The comparison results favor the hybrid structure. the selection of a hybrid configuration, however, is dependent on the costs of the hardware components and the avilable technology. A correct combination will allow one to design an optimal cost/performance parallel database system.