The demand for computational resources is increasing exponentially due to an increasing amount of digital services. Cloud computing is becoming the standard for enterprises to provide these resources. This resulted in hyperscalers which consist of a large number of servers. Data centers consume more than 1% of the world’s electrical energy. Therefore, many techniques are developed that both help to fulfil the needs of digital services and reduce the energy consumption of data center services. Modern-day servers can switch between different operating states which are often integrated in a power configuration mode of servers known as eco-mode. One of these techniques throttles the clock frequency of a central processing unit (CPU) which enables the possibility to lower the power needed for that CPU. This technique is known as dynamic frequency and voltage scaling (DFVS) and the states it switches between are known as performance states (P-states). A different technique that is integrated with eco-mode is the ability to switch between different idle states of the CPU. These states define whether certain caches of the CPU are flushed or not to conserve energy. These states are known as core states (C-states). A different approach that is focused on conserving energy is virtualisation within data centers. Virtualisation enables one physical server to host multiple virtual instances of servers (virtual machines). This reduces resource wastage and energy consumption of a data center. However, this creates the need for a strategic placement that ensures that the demands of the virtual machines are met and that minimises energy consumption and resource wastage. This thesis analyses four of these techniques: the best fit decreasing (BFD) algorithm, the integer linear programming (ILP) algorithm, the particle swarm optimisation (PSO) algorithm and the genetic algorithm (GA). This thesis provides a framework that uses a holistic approach to provide insights into the effects of using eco-mode of servers within the dynamics of virtual machine placement in data centers. This framework serves as a first step in parameterising the dynamics of a data center regarding its energy consumption and performance. The results show a potential energy reduction of up to approximately 20% with negligible impact on a data center’s performance. This result occurs when applying the best fit decreasing algorithm and having a server with an energy-efficient eco-mode. However, this thesis does not cover all parameters that play a role in the data center’s performance and energy consumption, so more research on this area is recommended.

In this study multiple design approaches have been tried to accurately determine the respiratory rate every 30 seconds of a patient in a hospital bed using six piezoelectric pressure sensors located sand­wiched between the mattress and the bed. After four design iterations using a variety of different methods, a final design using a Least Mean Squares (LMS) filter and peak detection in the frequency domain was implemented. According to the test results, this method determines an accurate respira­tory rate 85% of the time in a controlled, experimental environment with the subject lying completely still. Unfortunately this accuracy is below the set requirements. However, it is believed that multiple improvements can still be made on the algorithm to increase the accuracy.