Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

SIMULATIONS OF NEWTONIAN AND NON-NEWTONIAN FLOWS IN DEFORMABLE TUBES

Computational models for the investigation of flows in deformable tubes are developed and implemented in the open source computing environment OpenFOAM. Various simulations for Newtonian and non-Newtonian fluids under various flow conditions are carried out and analyzed. First, simulations are performed to investigate the flow of a shear-thinning, non-Newtonian fluid in a collapsed elastic tube and comparisons are made with experimental data. The fluid is modeled by means of the Bird-Carreau viscosity law. The computational domain of the deformed tube is constructed from data obtained via computer tomography imaging. Comparison of the computed velocity fields with the ultrasound Doppler velocity profile measurements show good agreement, as does the adjusted pressure drop along the tube's axis. Analysis of the shear rates show that the shear-thinning effect of the fluid becomes relevant in the cross-sections with the biggest deformation. The peristaltic motion is simulated by means of upper and lower rollers squeezing the fluid along a tube. Two frames of reference are considered. In the moving frame the computational domain is fixed and the coordinate system is moving with the roller speed, and in the fixed frame the roller is represented by a deforming mesh. Several two-dimensional simulations are carried out for Newtonian and non-Newtonian fluids. The effect of the shear-thinning behavior of the fluid on the transport efficiency is examined. In addition, the influence of the roller speed and the gap width between the rollers on the xxvii transport efficiency is discussed. Comparison with experimental data is also presented and different types of moving waves are implemented. In addition, the influence of the roller speed and the gap width between the rollers on the transport efficiency is discussed. Comparison with experimental data is also presented and different types of moving waves are implemented.

REAL TIME FUZZY CONTROLLER FOR QUADROTOR STABILITY CONTROL

In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM CortexM4 based controller board. We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.