The efficient processing of database applications on computing systems with multi-tiered persistent storage devices needs specialized algorithms to create optimal persistent storage management plans. A correct allocation and deallocation of multi-tiered persistent storage may significantly improve the overall performance of data processing. This paper describes the new algorithms that create allocation and deallocation plans for computing systems with multi-tiered persistent storage devices. One of the main contributions of this paper is an extension and application of a notation of Petri nets to describe the data flows in multi-tiered persistent storage. This work assumes a pipelined data processing model and uses a formalism of extended Petri nets to describe the data flows between the tiers of persistent storage. The algorithms presented in the paper perform linearization of the extended Petri nets to generate the optimal persistent storage allocation/deallocation plans. The paper describes the experiments that validate the data allocation/deallocation plans for multitiered persistent storage and shows the improvements in performance compared with the random data allocation/deallocation plans.
Automated performance tuning of data management systems offer various benefits such as improved performance, declined administration costs, and reduced workloads to database administrators (DBAs). Currently, DBAs tune the performance of database systems with a little help from the database servers. In this paper, we propose a new technique for automated performance tuning of data management systems. Firstly, we show how to use the periods of low workload time for performance improvements in the periods of high workload time. We demonstrate that extensions of a database system with materialised views and indices when a workload is low may contribute to better performance for a successive period of high workload. The paper proposes several online algorithms for continuous processing of estimated database workloads and for the discovery of the best plan for materialised view and index database extensions and of elimination of the extensions that are no longer needed. We present the results of experiments that show how the proposed automated performance tuning technique improves the overall performance of a data management system.
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