Techn. Hochsch., Diss.--Aachen, 2006.

Zugl.: Hagen, Fernuniv., Diss., 2007

Thesis (doctoral)--Humboldt-Universität, Berlin, 2005.

Fern-Universiẗat, Diss.--Hagen, 2007.

Univ., Diss.--Berlin, 2005. / Literaturverz. S. [363] - 387.

Zugl.: Münster (Westfalen), Universiẗat, Diss., 2009.

Thesis--Tübingen Universität, 2004/2005.

Formulary apportionment is an intensively debated mechanism for allocating tax base within multinational groups. Systems under which the formula is identical in all jurisdictions and systems under which jurisdictions can determine the weights on the formula factors individually can be observed. The latter systems produce uncertainty about the overall tax-liable share of the future group tax base. Counter-intuitively, I identify scenarios under which increased uncertainty leads to higher expected future group income. My results provide helpful insights for firms and policy makers debating the specific design of a formulary apportionment system. (author's abstract) / Series: WU International Taxation Research Paper Series

Databases as a service (DBaaS) transfer the advantages of cloud computing to data management systems, which is important for the big data era. The allocation in a DBaaS system, i.e., the mapping from databases to nodes of the infrastructure, influences performance, utilization, and cost-effectiveness of the system. Modeling databases and the underlying infrastructure as weighted graphs and using graph partitioning and mapping algorithms yields an allocation strategy. However, graph partitioning assumes that individual vertex weights add up (linearly) to partition weights. In reality, performance does usually not scale linearly with the amount of work due to contention on the hardware, on operating system resources, or on DBMS components. To overcome this issue, we propose an allocation strategy based on penalized graph partitioning in this paper. We show how existing algorithms can be modified for graphs with non-linear partition weights, i.e., vertex weights that do not sum up linearly to partition weights. We experimentally evaluate our allocation strategy in a DBaaS system with 1,000 databases on 32 nodes.