Os atuais sistemas de HPC tem realizado simulações mais complexas possíveis, produzindo benefícios para diversas áreas de pesquisa. Para atender à crescente demanda de processamento dessas simulações, novos equipamentos estão sendo projetados, visando à escala exaflops. Um grande desafio para a construção destes sistemas é a potência que eles vão demandar, onde perspectivas atuais alcançam GigaWatts. Para resolver este problema, esta tese apresenta uma abordagem para aumentar a eficiência energética usando recursos de HPC, objetivando reduzir os efeitos do desequilíbrio de carga e economizar energia. Nós desenvolvemos uma estratégia baseada no consumo de energia, chamada ENERGYLB, que considera características da plataforma, irregularidade e dinamicidade de carga das aplicações para melhorar a eficiência energética. Nossa estratégia leva em conta carga computacional atual e a frequência de clock dos cores, para decidir entre chamar uma estratégia de balanceamento de carga que reduz o desequilíbrio de carga migrando tarefas, ou usar técnicas de DVFS par ajustar as frequências de clock dos cores de acordo com suas cargas computacionais ponderadas. Como as diferentes arquiteturas de processador podem apresentam dois níveis de granularidade de DVFS, DVFS-por-chip ou DVFS-por-core, nós criamos dois diferentes algoritmos para a nossa estratégia. O primeiro, FG-ENERGYLB, permite um controle fino da frequência dos cores em sistemas que possuem algumas dezenas de cores e implementam DVFS-por-core. Por outro lado, CG-ENERGYLB é adequado para plataformas de HPC composto de vários processadores multicore que não permitem tal refinado controle, ou seja, que só executam DVFS-por-chip. Ambas as abordagens exploram desbalanceamentos residuais em aplicações interativas e combinam balanceamento de carga dinâmico com técnicas de DVFS. Assim, eles reduzem a frequência de clock dos cores com menor carga computacional os quais apresentam algum desequilíbrio residual mesmo após as tarefas serem remapeadas. Nós avaliamos a aplicabilidade das nossas abordagens utilizando o ambiente de programação paralela CHARM++ sobre benchmarks e aplicações reais. Resultados experimentais presentaram melhorias no consumo de energia e na demanda potência sobre algoritmos do estado-da-arte. A economia de energia com ENERGYLB usado sozinho foi de até 25% com nosso algoritmo FG-ENERGYLB, e de até 27% com nosso algoritmo CG-ENERGYLB. No entanto, os desequilíbrios residuais ainda estavam presentes após as serem tarefas remapeadas. Neste caso, quando as nossas abordagens foram empregadas em conjunto com outros balanceadores de carga, uma melhoria na economia de energia de até 56% é obtida com FG-ENERGYLB e de até 36% com CG-ENERGYLB. Estas economias foram obtidas através da exploração do desbalanceamento residual em aplicações interativas. Combinando balanceamento de carga dinâmico com DVFS nossa estratégia é capaz de reduzir a demanda de potência média dos sistemas paralelos, reduzir a migração de tarefas entre os recursos disponíveis, e manter o custo de balanceamento de carga baixo. / Current HPC systems have made more complex simulations feasible, yielding benefits to several research areas. To meet the increasing processing demands of these simulations, new equipment is being designed, aiming at the exaflops scale. A major challenge for building these systems is the power that they will require, which current perspectives reach the GigaWatts. To address this problem, this thesis presents an approach to increase the energy efficiency using of HPC resources, aiming to reduce the effects of load imbalance to save energy. We developed an energy-aware strategy, called ENERGYLB, which considers platform characteristics, and the load irregularity and dynamicity of the applications to improve the energy efficiency. Our strategy takes into account the current computational load and clock frequency, to decide whether to call a load balancing strategy that reduces load imbalance by migrating tasks, or use Dynamic Voltage and Frequency Scaling (DVFS) technique to adjust the clock frequencies of the cores according to their weighted loads. As different processor architectures can feature two levels of DVFS granularity, per-chip DVFS or per-core DVFS, we created two different algorithms for our strategy. The first one, FG-ENERGYLB, allows a fine control of the clock frequency of cores in systems that have few tens of cores and feature per-core DVFS control. On the other hand, CGENERGYLB is suitable for HPC platforms composed of several multicore processors that do not allow such a fine-grained control, i.e., that only perform per-chip DVFS. Both approaches exploit residual imbalances on iterative applications and combine dynamic load balancing with DVFS techniques. Thus, they reduce the clock frequency of underloaded computing cores, which experience some residual imbalance even after tasks are remapped. We evaluate the applicability of our approaches using the CHARM++ parallel programming system over benchmarks and real world applications. Experimental results present improvements in energy consumption and power demand over state-of-the-art algorithms. The energy savings with ENERGYLB used alone were up to 25%with our FG-ENERGYLB algorithm, and up to 27%with our CG-ENERGYLB algorithm. Nevertheless, residual imbalances were still present after tasks were remapped. In this case, when our approaches were employed together with these load balancers, an improvement in energy savings of up to 56% is achieved with FG-ENERGYLB and up to 36% with CG-ENERGYLB. These savings were obtained by exploiting residual imbalances on iterative applications. By combining dynamic load balancing with the DVFS technique, our approach is able to reduce the average power demand of parallel systems, reduce the task migration among the available resources, and keep load balancing overheads low.

The user growth in Internet services for the past years has caused a need to re-think methods for user authentication, authorization and accounting for network providers. To deal with this growing demand for Internet services, the underlying user authentication systems have to be able to, among other things, handle load spikes. This can be achieved by using loadbalancing, and there are both adaptive and non-adaptive methods of loadbalancing. This case study compares adaptive and non-adaptive loadbalancing for user authentication in terms of average throughput. To do this we set up a lab where we test two different load-balancing methods; a non-adaptive and a adaptive. The non-adaptive load balancing method is simple, only using a pool of servers to direct the load to in a round-robin way, whereas the adaptive load balancing method tries to direct the load using a calculation of the previous requests.

Self-balancing robots are unique mobile platforms that stay upright on two wheels using a closed-loop control system. They can turn on the spot using differential steering and have compact form factors that limit their required floor space. However they have major limitations keeping them from being used in real world applications: they cannot stand-up on their own, climb stairs, or overcome obstacles. They can fall easily if hit or going onto a slippery surface because they rely on friction for balancing. The first part of this research proposes a novel design to address the above mentioned issues related to stair-climbing, standing-up, and obstacles. A single revolute joint is added to the centre of a four-wheel drive robot onto which an arm is attached, allowing the robot to successfully climb stairs and stand-up on its own from a single motion. A model and simulation of the balancing and stair-climbing process are derived, and compared against experimental results with a custom robot prototype. The second part, a control system for an inverted pendulum equipped with a gyroscopic mechanism, was investigated for integration into self-balancing robots. It improves disturbance rejection during balance, and keeps equilibrium on slippery surfaces. The model of a gyroscope mounted onto an actuated gimbal was derived and simulated. To prove the concept worked, a custom-built platform showed it is possible for a balancing robot to stay upright with zero traction under the wheels.

Load management is an essential and important factor for distributed simulations running on shared resources due to load imbalances that can caused considerable performance loss. High Level Architecture (HLA) -based simulation is a framework that works to facilitate the design and management of distributed simulations. HLA coordinates the interaction between simulation entities (federates). However, HLA-based simulation standards do not present the ability to manage resources or help detect load imbalances that could directly cause decrease of performance. Focusing on this constraint, a migration-aware dynamic balancing system has been designed for HLA simulations to offer an efficient load-balancing scheme that works in large-scale environments. This system presents some limitations on estimating costs and benefits, so we propose an enhancement to this existing load balancing system, which improves the accuracy of estimating the number of migrations for the next load redistribution. The proposed scheme detects the load imbalances by evaluating the recourses overhead. The scheme classifies the recourses based on the overhead as overloaded and underloaded, followed by matching the highest overloaded recourses with the lowest underloaded recourses. Furthermore, the proposed scheme aims to precisely estimate the number of migrations by evaluating and analyzing the recourses to obtain the best number of migrations. Therefore, certain migrations that do not contribute to an improvement in the simulation performance are avoided. This avoidance is based on comparing time delay and time gain. Moreover, to be considered for migration, the overall sum of the time gains should be larger than the overall sum of the time delays. The proposed scheme has shown an improvement on decreasing the execution time.

The thesis investigates the importance of balance sheet in the Swiss financial reporting. At first it shows, on a theoretical basis, which is represented by balancing theories, the essential role of the balance sheet for recipients of accounting information. It also deals with the accounting legislation in Switzerland, general accounting principles, financial statement and its appurtenances and projection of selected balance sheet items. Moreover it examines the information capability of the balance sheet on the practical example of a Swiss company.

Current state-of-the-art walking controllers for humanoid robots use simple models, such as Linear Inverted Pendulum Mode (LIPM), to approximate Center of Mass(CoM) dynamics of a robot. These models are then used to generate CoM trajectories that keep the robot balanced while walking. Such controllers need prior information of foot placements, which is generated by a walking pattern generator. While the robot is walking, any change in the goal position leads to aborting the existing foot placement plan and re-planning footsteps, followed by CoM trajectory generation. This thesis proposes a tightly coupled walking pattern generator and a reactive balancing controller to plan and execute one step at a time. Walking is an emergent behavior from such a controller which is achieved by applying a virtual force in the direction of the goal. This virtual force, along with external forces acting on the robot, is used to compute desired CoM acceleration and the footstep parameters for only the next step. Step location is selected based on the capture point, which is a point on the ground at which the robot should step to stay balanced. Because each footstep location is derived as needed based on the capture point, it is not necessary to compute a complete set of footsteps. Experiments show that this approach allows for simpler inputs, results in faster operation, and is inherently immune to external perturbing and other reaction forces from the environment. Experiments are performed on Boston Dynamic's Atlas robot and NASA's Valkyrie R5 robot in simulation, and on Atlas hardware.

The aim of this thesis is to develop a load balancing tool for OpenFlow networks. Software-defined networking (SDN) principles are introduced (OpenFlow protocol used as an example) and compared to the legacy routing and switching technology. Openflow is the first protocol/API enabling communication between the control and infrastructure planes of the software-defined networking model. Key features of the protocol are described and several OpenFlow controllers are introduced. Current best practices in computer networks load balancing are discussed as well. The load balancing application development process is described including the test laboratory setups - Mininet (SW) and OFELIA (HW). The application test results are evaluated and possible further enhancements to the program are discussed.

This paper deals with the possibilities for load balancing and clustering of an existing server for speech processing. The paper analyzes problems of load balancing and clustering. There are also described the concepts of network programming and options for I/O processing. A new design of a load balancer is created, fully customized for the needs of speech processing server. This newly designed load balancer is implemented and thoroughly tested.

This diploma thesis is focused on issues of economic diplomacy of ASEAN countries, depending on the conflict in the South China Sea. The first part describes the development of this conflict and explains possible interactions between ASEAN countries and China through the concepts of balancing, bandwagoning and hedging. It arouses the theoretical assumptions of the reactions of the states to the deteriorating security situation in the South China Sea. The practical analytical part is dedicated to the exploration of economic relations between China and ASEAN countries quantitatively through the use of statistical data as well as qualitatively. Finally, the main results are interpreted and the conclusions drawn.

Current state-of-the-art walking controllers for humanoid robots use simple models, such as Linear Inverted Pendulum Mode (LIPM), to approximate Center of Mass(CoM) dynamics of a robot. These models are then used to generate CoM trajectories that keep the robot balanced while walking. Such controllers need prior information of foot placements, which is generated by a walking pattern generator. While the robot is walking, any change in the goal position leads to aborting the existing foot placement plan and re-planning footsteps, followed by CoM trajectory generation. This thesis proposes a tightly coupled walking pattern generator and a reactive balancing controller to plan and execute one step at a time. Walking is an emergent behavior from such a controller which is achieved by applying a virtual force in the direction of the goal. This virtual force, along with external forces acting on the robot, is used to compute desired CoM acceleration and the footstep parameters for only the next step. Step location is selected based on the capture point, which is a point on the ground at which the robot should step to stay balanced. Because each footstep location is derived as needed based on the capture point, it is not necessary to compute a complete set of footsteps. Experiments show that this approach allows for simpler inputs, results in faster operation, and is inherently immune to external perturbing and other reaction forces from the environment. Experiments are performed on Boston Dynamic's Atlas robot and NASA's Valkyrie R5 robot in simulation, and on Atlas hardware.