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Cloud computing is a model for enabling on-demand access to a shared pool of computing resources. With virtually limitless on-demand resources, a cloud environment enables the hosted Internet application to quickly cope when there is an increase in the workload. However, the overhead of provisioning resources exposes the Internet application to periods of under-provisioning and performance degradation. Moreover, the performance interference, due to the consolidation in the cloud environment, complicates the performance management of the Internet applications. In this dissertation, we propose two approaches to mitigate the impact of the resources provisioning overhead. The first approach employs control theory to scale resources vertically and cope fast with workload. This approach assumes that the provider has knowledge and control over the platform running in the virtual machines (VMs), which limits it to Platform as a Service (PaaS) and Software as a Service (SaaS) providers. The second approach is a customer-side one that deals with the horizontal scalability in an Infrastructure as a Service (IaaS) model. It addresses the trade-off problem between cost and performance with a multi-goal optimization solution. This approach finds the scale thresholds that achieve the highest performance with the lowest increase in the cost. Moreover, the second approach employs a proposed time series forecasting algorithm to scale the application proactively and avoid under-utilization periods. Furthermore, to mitigate the interference impact on the Internet application performance, we developed a system which finds and eliminates the VMs suffering from performance interference. The developed system is a light-weight solution which does not imply provider involvement. To evaluate our approaches and the designed algorithms at large-scale level, we developed a simulator called (ScaleSim). In the simulator, we implemented scalability components acting as the scalability components of Amazon EC2. The current scalability implementation in Amazon EC2 is used as a reference point for evaluating the improvement in the scalable application performance. ScaleSim is fed with realistic models of the RUBiS benchmark extracted from the real environment. The workload is generated from the access logs of the 1998 world cup website. The results show that optimizing the scalability thresholds and adopting proactive scalability can mitigate 88% of the resources provisioning overhead impact with only a 9% increase in the cost.
The dissertation examines the use of performance information by public managers. “Use” is conceptualized as purposeful utilization in order to steer, learn, and improve public services. The main research question is: Why do public managers use performance information? To answer this question, I systematically review the existing literature, identify research gaps and introduce the approach of my dissertation. The first part deals with manager-related variables that might affect performance information use but which have thus far been disregarded. The second part models performance data use by applying a theory from social psychology which is based on the assumption that this management behavior is conscious and reasoned. The third part examines the extent to which explanations of performance information use vary if we include others sources of “unsystematic” feedback in our analysis. The empirical results are based on survey data from 2011. I surveyed middle managers from eight selected divisions of all German cities with county status (n=954). To analyze the data, I used factor analysis, multiple regression analysis, and structural equation modeling. My research resulted in four major findings: 1) The use of performance information can be modeled as a reasoned behavior which is determined by the attitude of the managers and of their immediate peers. 2) Regular users of performance data surprisingly are not generally inclined to analyze abstract data but rather prefer gathering information through personal interaction. 3) Managers who take on ownership of performance information at an early stage in the measurement process are also more likely to use this data when it is reported to them. 4) Performance reports are only one source of information among many. Public managers prefer verbal feedback from insiders and feedback from external stakeholders over systematic performance reports. The dissertation explains these findings using a deductive approach and discusses their implications for theory and practice.
To date, positive relationships between diversity and community biomass have been mainly found, especially in terrestrial ecosystems due to the complementarity and/or dominance effect. In this thesis, the effect of diversity on the performance of terrestrial plant and phytoplankton communities was investigated to get a better understanding of the underlying mechanisms in the biodiversity-ecosystem functioning context. In a large grassland biodiversity experiment, the Jena Experiment, the effect of community diversity on the individual plant performance was investigated for all species. The species pool consisted of 60 plant species belonging to 4 functional groups (grasses, small herbs, tall herbs, legumes). The experiment included 82 large plots which differed in species richness (1-60), functional richness (1-4), and community composition. Individual plant height increased with increasing species richness suggesting stronger competition for light in more diverse communities. The aboveground biomass of the individual plants decreased with increasing species richness indicating stronger competition in more species-rich communities. Moreover, in more species-rich communities plant individuals were less likely to flower out and had fewer inflorescences which may be resulting from a trade-off between resource allocation to vegetative height growth and to reproduction. Responses to changing species richness differed strongly between functional groups and between species of similar functional groups. To conclude, individual plant performance can largely depend on the diversity of the surrounding community. Positive diversity effects on biomass have been mainly found for substrate-bound plant communities. Therefore, the effect of diversity on the community biomass of phytoplankton was studied using microcosms. The communities consisted of 8 algal species belonging to 4 functional groups (green algae, diatoms, cyanobacteria, phytoflagellates) and were grown at different functional richness levels (1-4). Functional richness and community biomass were negatively correlated and all community biomasses were lower than their average monoculture biomasses of the component species, revealing community underyielding. This was mainly caused by the dominance of a fast-growing species which built up low biomasses in monoculture and mixture. A trade-off between biomass and growth rate in monoculture was found for all species, and thus fast-growing species built up low biomasses and slow-growing species reached high biomasses in monoculture. As the fast-growing, low-productive species monopolised nutrients in the mixtures, they became the dominant species resulting in the observed community underyielding. These findings suggest community overyielding when biomasses of the component species are positively correlated with their growth rates in monocultures. Aquatic microcosm experiments with an extensive design were performed to get a broad range of community responses. The phytoplankton communities differed in species diversity (1, 2, 4, 8, and 12), functional diversity (1, 2, 3, and 4) and community composition. The species/functional diversity positively affected community biomass, revealing overyielding in most of the communities. This was mainly caused by a positive complementarity effect which can be attributed to resource use complementarity and/or facilitative interaction among the species. Overyielding of more diverse communities occurred when the biomass of the component species was correlated positively with their growth rates in monoculture and thus, fast-growing and high-productive species were dominant in mixtures. This and the study mentioned above generated an emergent pattern for community overyielding and underyielding from the relationship between biomass and growth rate in monoculture as long as the initial community structure prevailed. Invasive species can largely affect ecosystem processes, whereas invasion is also influenced by diversity. To date, studies revealed negative and positive diversity effects on the invasibility (susceptibility of a community to the invasion by new species). The effect of productivity (nutrient concentration ranging from 10 to 640 µg P L-1), herbivory (presence/absence of the generalist feeder) and diversity (3, 4, 6 species were randomly chosen from the resident species pool) on the invasibility of phytoplankton communities consisting of 10 resident species was investigated using semi-continuous microcosms. Two functionally diverse invaders were chosen: the filamentous and less-edible cynaobacterium C. raciborskii and the unicellular and well-edible phytoflagellate Cryptomonas sp. The phytoflagellate indirectly benefited from grazing pressure of herbivores whereas C. raciborskii suffered more from it. Diversity did not affect the invasibility of the phytoplankton communities. Rather, it was strongly influenced by the functional traits of the resident and invasive species.