Parallel database systems are increasingly being deployed to support the performance demands of end-users. While declustering data across multiple nodes facilitates parallelism, existing data placement may no longer be optimal due to skewed workloads and changing access patterns. To prevent performance degradation, the placement of data must be reorganized, and this must be done on-line to minimize disruption to the system. In this paper, we consider a dynamic self-tuning approach to reorganization in a shared nothing system. We introduce a new index-based method that faciliates fast and e#- cient migration of data. Our solution incorporates a globally height-balanced structure and load tracking at di#erent levels of granularity. We conducted an extensive performance study, and implemented the methods on the Fujitsu AP3000 machine. Both the simulation and empirical results demonstrate that our proposed method is indeed scalable and e#ective in correcting any deterioration in system throughput. 1.

With today’s demands for continuous avail-ability of mission-critical databases, on-line reorganization is a necessity. In this paper we present a new on-Iine reorganization algo-rithm which defers secondary index updates and piggybacks them with user transactions. In addition to the significant reduction of the total I/O cost, the algorithm also assures that almost all the database is available all of the time and that the reorganization is interrupt-ible and restartable. We believe that the tech-nique presented in this paper could be used for improving normal database update perfor-mance as well. 1

this paper, we propose online heat balancing strategies for Btree index over parallel shared nothing machines based on the principle of distributing the given heat as evenly as possible across the system PEs. Furthermore, the proposed strategies have the capability to reduce the instantaneous migration cost during the heat balancing process, which in turn avoid the harmful migrations that may degrade the system performance. We evaluate the performance of the proposed strategies in comparison with the well-known strategies. The conducted simulation shows their efficiency in correcting the system performance degradation.

In shared-nothing environments, data is typically declustered and indexed across the system processing elements (PEs) to achieve efficient processing. However access patterns are inherently dynamic and skewed, thus, data reorganization based on the data access history (heat) is essential and should be done online. While the data is being reorganized, indexes need to be modified too, therefore, reorganization should additionally deal with the index modification. Based on minimization of index modification, we propose a data reorganization technique over a shared-nothing parallel system. By finding the exact work that should be done, the technique can smoothly balance a given heat across the PEs as fast as possible, if it is required. By tuning its parameters, it can cover a wide range of balancing requirements. We evaluate its performance through simulation studies. Its effectiveness is clarified quantitatively. 1