• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.801-812
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• 2002

On-line reorganization in a shared nothing database cluster is crucial to the performance of the database system in a dynamic environment like WWW where the number of users grows rapidly and changing access patterns may exhibit high skew. In the existing method of on-line reorganization have a drawback that needs excessive data migrations in case more than two nodes within a cluster have overload at the same time. In this paper, we propose an advanced B$^{+}$ tree based on-line reorganization method that solves data skew on multi-nodes. Our method facilitates fast and efficient data migration by including spare nodes that are added to cluster through on-line scaling. Also we apply CSB$^{+}$ tree (Cache Sensitive B$^{+}$ tree) to our method instead of B$^{+}$ tree for fast select and update queries. We conducted performance study and implemented the method on Ultra Fault-Tolerant Database Cluster developed for high scalability and availability. Empirical results demonstrate that our proposed method is indeed effective and fast than the existing method. method.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.935-948
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• 2003

An online scaling method adds new nodes to the shared-nothing database cluster and makes tables be reorganized while the system is running. The objective is to share the workload with many nodes and increase the capacity of cluster systems. The existing online scaling method, however, has two problems. One is the degradation of response time and transactions throughput due to the additional overheads of data transfer and replica's condidtency. The other is and inefficient recovery mechanism in which the overall scaling transaction is aborted by a fault. These problems deteriorate the availability of shared-nothing database cluster. To avoid the additional overheads throughout the scaling period, our scalingmethod consists of twophases : a parallel data transfer phase and a combination phase. The parallel data transferred datausing reduces the size of data transfer by dividing the data into the number of replicas. The combination phase combines the transferred datausing resources of spare nodes. Also, our method reduces the possibility of failure throughout the scaling period and improves the availability of the database cluster.