Scalability and availability are key features of parallel database systems. To realize scalability, many dynamic load-balancing methods with data placement and parallel index structures on shared-nothing parallel infrastructure have been proposed. Data migration with rangepartitioned placement using a parallel Btree is one solution. The combination of range partitioning and chained declustered replicas provides high availability while preserving scalability. However, independent treatment of the primary and backup data in each node results in long failover times. We propose a novel method for compound treatment of chained declustered replicas using a parallel Btree, called the Fat-Btree. In the proposed method, the single Fat-Btree provides access paths to both primary and backup data in all processor elements, which greatly reduces failover time. Moreover, it enables dynamic load balancing without physical data migration, and improves memory space utilization for processing the index. Experiments using PostgreSQL on a 160-node PC cluster demonstrate the effect.
In this paper, we discuss access forwarding schemes for the replication that achieve balanced access load distribution for data replicas on multiple storage nodes. In
SUMMARYIn this paper, we propose a nonstop system upgrade method without significant performance degradation for data management software. To reduce disk accesses and network traffic, we construct logical nodes inside a physical node and migrate data between the symbiotic logical nodes. This logical migration is assisted by storage management functions that hide data location and migration from users. We also show the effectiveness of our method using experimental results on the system based on the Autonomous Disks we have proposed as a highly available storage system technology.
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