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Today’s cloud data centers consume an enormous amount of energy, and energy consumption will rise in the future. An estimate from 2012 found that data centers consume about 30 billion watts of power, resulting in about 263TWh of energy usage per year. The energy consumption will rise to 1929TWh until 2030. This projected rise in energy demand is fueled by a growing number of services deployed in the cloud. 50% of enterprise workloads have been migrated to the cloud in the last decade so far. Additionally, an increasing number of devices are using the cloud to provide functionalities and enable data centers to grow. Estimates say more than 75 billion IoT devices will be in use by 2025.
The growing energy demand also increases the amount of CO2 emissions. Assuming a CO2-intensity of 200g CO2 per kWh will get us close to 227 billion tons of CO2. This emission is more than the emissions of all energy-producing power plants in Germany in 2020.
However, data centers consume energy because they respond to service requests that are fulfilled through computing resources. Hence, it is not the users and devices that consume the energy in the data center but the software that controls the hardware. While the hardware is physically consuming energy, it is not always responsible for wasting energy. The software itself plays a vital role in reducing the energy consumption and CO2 emissions of data centers. The scenario of our thesis is, therefore, focused on software development.
Nevertheless, we must first show developers that software contributes to energy consumption by providing evidence of its influence. The second step is to provide methods to assess an application’s power consumption during different phases of the development process and to allow modern DevOps and agile development methods. We, therefore, need to have an automatic selection of system-level energy-consumption models that can accommodate rapid changes in the source code and application-level models allowing developers to locate power-consuming software parts for constant improvements. Afterward, we need emulation to assess the energy efficiency before the actual deployment.
This thesis gives an overview over mathematical modeling of complex fluids with the discussion of underlying mechanical principles, the introduction of the energetic variational framework, and examples and applications. The purpose is to present a formal energetic variational treatment of energies corresponding to the models of physical phenomena and to derive PDEs for the complex fluid systems. The advantages of this approach over force-based modeling are, e.g., that for complex systems energy terms can be established in a relatively easy way, that force components within a system are not counted twice, and that this approach can naturally combine effects on different scales. We follow a lecture of Professor Dr. Chun Liu from Penn State University, USA, on complex fluids which he gave at the University of Wuerzburg during his Giovanni Prodi professorship in summer 2012. We elaborate on this lecture and consider also parts of his work and publications, and substantially extend the lecture by own calculations and arguments (for papers including an overview over the energetic variational treatment see [HKL10], [Liu11] and references therein).