The following master thesis is focused on creation of methodology for optimal usage of load balancing in data center environment. Thesis is divided into eight chapters. The first chapter describes the reasons why to deal with this topic further. The second chapter summarizes the state of load balancing. This chapter is based on research of already elaborated thesis which were focused on load balancing in different ways. The third chapter summarizes load balancing including its key principles. The fourth chapter describes an actual state of load balancing in data center environment. An observation of real usage of load balancing in selected data center was used for the main purpose of this chapter. The fifth chapter consists of analysis of the currently existing methodologies which are used from the infrastructure projects purpose. The sixth chapter deals with creation of methodology for optimal usage of load balancing in data center. The seventh chapter evaluates usage of methodology by applying of this methodology to real practical example of implementation of load balancing. The eighth chapter summarizes all detected conclusions.

L'objectif de cette étude est de concevoir un système de refroidissement passif au sein d'un data center. La solution qui a été choisie est la boucle thermosiphon, combinant le free cooling et le refroidissement par changement de phase. Cette technologie offre de la simplicité et de la compacité. De plus, en l'associant avec des échangeurs de chaleur à micro-canaux, elle est capable d'absorber de grandes quantités de flux de chaleur avec un faible débit du réfrigérant. La boucle thermosiphon est composée d'un évaporateur à mini-canaux et à courants parallèles, d'un condenseur à air, d'un riser et d'un downcomer. Un prototype expérimental a été construit afin de caractériser les transferts de chaleur entre le réfrigérant et la chaleur créée. Des études expérimentales sont introduites. L'influence du taux de chargement et de la puissance électrique est détaillée et analysée. En parallèle, un modèle numérique a été développé pour prédire les caractéristiques du réfrigérant en fonction des paramètres géométriques et climatiques. Une comparaison avec les résultats expérimentaux est également effectuée. Enfin, la boucle thermosiphon est améliorée avec l'ajout d'un second évaporateur. Les tests sont effectués avec des puissances plus importantes. Une nouvelle conception d'une boucle thermosiphon et les limites du prototype sont présentées. / The objective of this study is to build a passive cooling system in a data center. The chosen solution is the loop thermosyphon, combining free cooling and two-phase cooling. This technology offers simplicity and compactness. Furthermore, by associating with micro-channels heat exchangers, it is able to remove higher heat fluxes while working with smaller mass flow rate of coolant. The thermosyphon is composed by mini-channel parallel-flow evaporator, an air condenser, a riser and a downcomer. The experimental setup has been built to characterize the heat transfer between the working fluid and the provided heat. An experimental study is introduced. The effect of the fill ratio and the input power is specified and analyzed. In parallel, a numerical model has been developed to predict the fluid properties in function of geometrical and climatic parameters. A comparison between experimental and numerical results is also carried out. Finally, the loop thermosyphon is upgraded with a second mini-channel parallel flow evaporator. Tests are conducted with huger heat flux. A new design of loop thermosyphon and the limits of the prototype are introduced.

Mission-critical facilities house operations that when interrupted, can prove disastrous to an organization’s future. Limited market research is available to determine what roof types are best suited to meet the unique demands of these buildings. The purpose of this research was to evaluate different roof materials and to observe trends relative to their lifecycle costs and roof professional’s assessment in use with mission-critical facilities. The objectives of the study were to determine the average annual lifecycle costs for the sampled roof materials, to determine the roofing professionals’ preferred mission-critical facility roof materials, and to priority rank the sampled roof materials for use with mission-critical facilities A pilot study was conducted to assess variables in evaluating different roof materials and their use with mission-critical facilities. Additionally, a survey was administered to roofing professionals across the United States to obtain lifecycle cost information for various roof materials as well as ratings for those materials for use with mission-critical facilities. The research found that single-ply roofs, with the exception of 60 Mil TPO, had lower annual lifecycle costs than built-up roofs due to their having lower install and removal costs, as well as having increasing life expectancies over the years. The metal roof selection was also shown to have a low annual lifecycle cost due to having the longest estimated lifespan. Built-up and metal roofs were rated highest by roofing professionals for their use with mission-critical facilities, suggesting a prioritization of risk reduction versus cost savings. When the lifecycle cost data was applied to the roof material ratings, the data showed that built-up roofs presented themselves as good values for mission-critical facilities; however, 90 Mil EPDM and 24-gauge metal roofs could be considered as viable cost savings alternatives.

IT equipment has been estimated to be responsible for 2% of global CO2 emissions and data centers are responsible for 1.2% of U.S. energy consumption. With the large quantity of high quality digital content available on the Internet the energy demands and environmental impact of the data centers must be addressed. The use of peer-to-peer technologies, such as BitTorrent, to distribute legal content to consumers is actively being explored as a means of reducing both file download times and the energy consumption of data centers. This approach pushes the energy use out of the data centers and into the homes of content consumers (who are also then content distributors). The current BitTorrent protocol requires that clients must be fully powered-on to be participating members in a swarm. In this thesis, an extension to the BitTorrent protocol that utilizes long-lived knowledge of sleeping peers to enable clients to sleep when not actively distributing content yet remain responsive swarm members is developed. New peer states and events required for the protocol extension, the implementation the new protocol in a simulation environment, and the implementation of the protocol extension in a real client are described. Experiments on a simulated swarm of 51 peers transferring a 1 GB and a real swarm of 11 peers transfer- ring a 100 MB file were run. To validate the simulation a simulated swarm of 11 peers transferring a 100 MB file is compared to the real swarm of 11 peers. The results of standard BitTorrent are compared to the new Green BitTorrent by examining download times, sleep time, and awake time. The results of the experiment show significant energy savings are possible with only a small penalty in download time. Energy savings of up to 75% are shown with download time increases as little as 10%. These energy savings could equate to over $1 billion dollars per year in the US alone if Green BitTorrent is used instead of standard BitTorrent for future rollouts of legal distribution systems.

Due to the ongoing climate crisis, reducing waste and carbon emissions has become hot topic in many fields of study. Cloud data centers contribute a large portion to the world’s energy consumption. In this work, methodologies are developed using machine learning algorithms to improve prediction of the energy consumption of a container in a data center. The goal is to share this information with the user ahead of time, so that the same can make educated decisions about their environmental footprint.This work differentiates itself in its sole focus on optimizing prediction, as opposed to other approaches in the field where energy modeling and prediction has been studied as a means to building advanced scheduling policies in data centers. In this thesis, a qualitative comparison between various machine learning approaches to energy modeling and prediction is put forward. These approaches include Linear, Polynomial Linear and Polynomial Random Forest Regression as well as a Genetic Algorithm, LSTM Neural Networks and Reinforcement Learning. The best results were obtained using the Polynomial Random Forest Regression, which produced a Mean Absolute Error of of 26.48% when run against data center metrics gathered after the model was built. This prediction engine was then integrated into a Proof of Concept application as an educative tool to estimate what metrics of a cloud job have what impact on the container power consumption.

Les travaux de cette thèse portent sur l'optimisation des flux énergétiques et informatiques dans un réseau intelligent ayant pour but d'alimenter un centre de calcul via des énergies renouvelables. Dans cette thèse sont traités les problèmes liés à la mise en commun des informations de types énergétique et informatique dans une contrainte de réactivité forte à travers la création d'une architecture pour un réseau intelligent. La modélisation d'un tel réseau doit permettre la prise de décision de manière dynamique et autonome. L'objectif de cette modélisation, via un réseau intelligent, est l'optimisation des ressources renouvelables afin de diminuer l'empreinte écologique. / The work of this thesis deals with the optimization of energy and computer flows in an intelligent network aiming to supply a data center via renewable energies. In this thesis are treated the problems related to the pooling of energy and computer information in a strong reactivity constraint through the creation of an architecture for an intelligent network. The modeling of such a network must allow the decision making in a dynamic and autonomous way. The objective of this modeling, via an intelligent network, is the optimization of renewable resources in order to reduce the ecological footprint.

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One of the crucial challenges for Data Center (DC) operation is inefficient thermal management which leads to excessive energy waste. The information technology (IT) equipment and cooling systems of a DC are major contributors to power consumption. Additionally, failure of a DC cooling system leads to higher operating temperatures, causing critical electronic devices, such as servers, to fail which leads to significant economic loss. Improvements can be made in two ways, through (1) better design of a DC architecture and (2) optimization of the system for better heat transfer from hot servers. Row-based cooling is a suitable DC configuration that reduces energy costs by improving airflow distribution. Here, the IT equipment is contained within an enclosure that includes a cooling unit which separates cold and back chambers to eliminate hot air recirculation and cold air bypass, both of which produce undesirable airflow distributions. Besides, due to scalability, ease of implementation, and operational cost, row-based systems have gained in popularity for DC computing applications. However, a general thermal model is required to predict spatiotemporal temperature changes inside the DC and properly apply appropriate strategies. As yet, only primitive tools have been developed that are time-consuming and provide unacceptable errors during extrapolative predictions. We address these deficiencies by developing a rapid, adaptive, and accurate hybrid model by combining a DDM and the thermofluid transport relations to predict temperatures in a DC. Our hybrid model has low interpolative prediction errors below 0.7 oC and extrapolative errors less than one half of black-box models. Additionally, by changing the studied DC configuration such as cooling unit fans and severs locations, there are a few zones with prediction error more than 2 oC. Existing methods for cooling unit fault detection and diagnosis (FDD) are designed to successfully overcome individually occurring faults but have difficulty handling simultaneous faults. We apply a gray-box model involves a case study to detect and diagnose cooling unit fan and pump failure in a row-based DC cooling system. Fast detection of anomalous behavior saves energy and reduces operational costs by initiating remedial actions. Cooling unit fans and pumps are relatively low-reliability components, where the failure of one or more components can cause the entire system to overheat. Therefore, appropriate energy-saving strategies depend largely on the accuracy and timeliness of temperature prediction models. We used our gray-box model to produce thermal maps of the DC airspace for single as well as simultaneous failure conditions, which are fed as inputs for two different data-driven classifiers, CNN and RNN, to rapidly predict multiple simultaneous failures. Our FDD strategy can detect and diagnose multiple faults with accuracy as high as 100% while requiring relatively few simultaneous fault training data samples. / Thesis / Candidate in Philosophy

The ever-growing demand for cyber-physical infrastructures has significantly affected worldwide energy consumption and environmental sustainability over the past two decades. Although the average heat load of the computing infrastructures has increased, the supportive capacity of cooling infrastructures requires further improvement. Consequently, energy-efficient cooling architectures, real-time load management, and waste heat utilization strategies have gained attention in the data center (DC) industry. In this dissertation, essential aspects of cooling system modularization, workload management, and waste-heat utilization were addressed. At first, benefits of several legacy and modular DCs were assessed from the viewpoint of the first and second laws of thermodynamics. A computational fluid dynamics simulation-informed thermodynamic energy-exergy formulation captured equipment-level inefficiencies for various cooling architectures and scenarios. Furthermore, underlying reasons and possible strategies to reduce dominant exergy loss components were suggested. Subsequently, strategies to manage cooling parameters and IT workload were developed for the DCs with rack-based and row-based cooling systems. The goal of these management schemes was to fulfill either single or multiple objectives such as energy, exergy, and computing efficiencies. Thermal models coupled to optimization problems revealed the non-trivial tradeoffs across various objective functions and operation parameters. Furthermore, the scalability of the proposed approach for a larger DC was demonstrated. Finally, a waste heat management strategy was developed for new-age infrastructures containing both air- and liquid-cooled servers, one of the critical issues in the DC industry. Exhaust hot water from liquid-cooled servers was used to drive an adsorption chiller, which in turn produced chilled water required for the air-handler units of the air-cooled system. This strategy significantly reduced the energy consumption of existing compression chillers. Furthermore, economic and environmental assessments were performed to discuss the feasibility of this solution for the DC community. The work also investigated the potential tradeoffs between waste heat recovery and computing efficiencies. / Thesis / Doctor of Philosophy (PhD)

Sub-ambient direct-on-chip liquid cooling is an emerging technology in the data center industry. The risk of an electrically conductive liquid leaking out to the electrical components and damaging the servers has been the major factor in holding back the use of liquid cooling historically. This technology effectively removes that risk. A direct-on-chip liquid cooling system, where average system pressure and average CPU temperatures can be fixed for a range of server computing loads and coolant supply temperatures for data center servers has been designed and constructed. This has been used to determine what impact pressure has on a small-scale liquid cooled server system in terms of CPU power consumption and CPU temperatures. The cooling system was only able to work with one server connected. Experiments with different values for the CPU temperature setpoint, coolant supply temperature setpoint, server computational load, and server pressure were executed to verify that the system works as intended. Applying a range of CPU computing loads works well, maintaining fixed average CPU temperatures works, with differences between the CPUs at higher temperatures and failure to reach average CPU temperatures when the difference between these and the coolant supply temperature is small. Maintaining fixed average pressure before the server works well, while pressure after the server is heavily affected by coolant flow. However, this effect is not seen as important for the experimental goals of the thesis. Maintaining a fixed coolant supply temperature works well with some slow fluctuations around the setpoint. No noticeable effects from pressure on CPU power consumption and CPU temperatures were seen. However, lower flow resistance was seen by the circulating pump when negative system pressure was lower which implies that less pump energy is needed to pump at lower negative pressure. The pressure was not in the region where the coolant could phase change during the experiments.