Data management in engineering design

Abstract

Engineering design involves the production of large volumes of data. These data are a sophisticated mix of high performance computational and experimental results, and must be managed, shared and distributed across worldwide networks. Given limited storage and networking bandwidth, but rapidly growing rates of data production, effective data management is becoming increasingly critical. Within the context of Airbus, a leading aerospace engineering company, this thesis bridges the gap between academia and industry in the management of engineering data. It explores the high performance computing (HPC) environment used in aerospace engineering design, about which little was previously known, and applies the findings to the specific problem of file system cleaning. The properties of Airbus HPC file systems show many similarities with other environments, such as workstations and academic or public HPC file systems, but there are also some notably unique characteristics. In this research study it was found that Airbusfile system volumes exhibit a greater disk usage by a smaller proportion of files than any other case, and a single file type accounts for 65% of the disk space but less than 1% of the files. The characteristics and retention requirements of this file type formed the basis of a new cleaning tool we have researched and deployed within Airbus that is cognizant of these properties, and yielded disk space savings of 21.1 TB (15.2%) and 37.5 TB (28.2%) over two cleaning studies, and may be able to extend the life of existing storage systems by up to 5.5 years. It was also noted that the financial value of the savings already made exceed the cost of this entire research programme. Furthermore, log files contain information about these key files, and further analysis reveals that direct associations can be made to infer valuable additional metadata about such files. These additional metadata were shown to be available for a significant proportion of the data, and could be used to improve the effectiveness and efficiency of future data management methods even further.

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