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Shared Disk database systems offer a high flexibility for parallel transaction and query processing. This is because each node can process any transaction, query or subquery because it has access to the entire database. Compared to Shared Nothing database systems, this is particularly advantageous for scan queries for which the degree of intra-query parallelism as well as the scan processors themselves can dynamically be chosen. On the other hand, there is the danger of disk contention between subqueries, in particular for index scans. We present a detailed simulation study to analyze the effectiveness of parallel scan processing in Shared Disk database systems. In particular, we investigate the relationship between the degree of declustering and the degree of scan parallelism for relation scans, clustered index scans, and non-clustered index scans. Furthermore, we study the usefulness of disk caches and prefetching for limiting disk contention. Finally, we show that disk contention in multi-user mode can be limited for Shared Disk database systems by dynamically choosing the degree of scan parallelism.

Automated Storage and Retrieval Systems (AS/RS) are core components of intralogistics systems. Revealing the potentials regarding energy efficiency and further optimizing these systems are of importance. A benchmarking procedure for AS/RS was developed to characterize parameters and specifications in order to derive energy efficiency indicators. The benchmarking procedure for AS/RS has recently been varified. A comprehensive test series was conducted to rate the energy efficiency of three AS/RS systems that are available on the market: Miniload-Crane, Horizontal Carousel-and Shuttle-Systems. This paper presents the investigation methods as well as the final results for the energy efficiency of all three AS/RS types based on comparable logistic performance.

Abstract. Data warehouse queries pose challenging performance problems that often necessitate the use of parallel database systems (PDBS). Although dynamic load balancing is of key importance in PDBS, to our knowledge it has not yet been investigated thoroughly for parallel data warehouses. In this study, we propose a scheduling strategy that simultaneously considers both processors and disks while utilizing the load balancing potential of a Shared Disk architecture. We compare the performance of this new method to several other approaches in a comprehensive simulation study, incorporating skew aspects and typical data warehouse features such as star schemas.