Mission-critical system typically refers to a project or system for which the success is vital to the mission of the underlying organization. The failure or delayed completion of the tasks in mission-critical systems may cause severe financial loss, even human casualties. For example, failure of an accurate and timely forecast of Hurricane Rita in September 2005 caused enormous financial loss and several deaths. As such, real-time guarantee and reliability have always been two key foci of mission-critical system design. Many factors affect real-time guarantee and reliability. From the software design perspective, which is the focus of this paper, three aspects are most important. The first of these is how to design a single application to effectively support real-time requirement and improve reliability, the second is how to integrate different applications in a cluster environment to guarantee real-time requirement and improve reliability, and the third is how to effectively coordinate distributed applications to support real-time requirements and improve reliability. Following these three aspects, this dissertation proposes and implements three novel methodologies: real-time component based single node application development, real-time workflow-based cluster application integration, and real-time distributed admission control. For ease of understanding, we introduce these three methodologies and implementations in three real-world mission-critical application systems: single node mission-critical system, cluster environment mission-critical system, and wide-area network mission-critical system. We study full-scale design and implementation of these mission-critical systems, more specifically: 1) For the single node system, we introduce a real-time component based application model, a novel design methodology, and based on the model and methodology, we implement a real-time component based Enterprise JavaBean (EJB) System. Through component based design, efficient resource management and scheduling, we show that our model and design methodology can effectively improve system reliability and guarantee real-time requirement. 2) For the system in a cluster environment, we introduce a new application model, a real-time workflow-based application integration methodology, and based on the model and methodology, we implement a data center management system for the Southeastern Universities Research Association (SURA) project. We show that our methodology can greatly simplify the design of such a system and make it easier to meet deadline requirements, while improving system reliability through the reuse of fully tested legacy models. 3) For the system in a wide area network, we narrow our focus to a representative VoIP system and introduce a general distributed real-time VoIP system model, a novel system design methodology, and an implementation. We show that with our new model and architectural design mechanism, we can provide effective real-time requirement for Voice over Internet Protocol (VoIP).

With the rapid growth of servers and applications spurred by the Internet economy, power consumption in today's data centers is reaching unsustainable limits. This has led to an imminent financial, technical and environmental crisis that is impacting the society at large. Hence, it has become critically important that power consumption be efficiently managed in these computing power-houses of today. In this work, we revisit the issue of adaptive power and performance management of data center server platforms. Traditional data center servers are statically configured and always over-provisioned to be able to handle peak load. We transform these statically configured data center servers to clairvoyant entities that can sense changes in the workload and dynamically scale in capacity to adapt to the requirements of the workload. The over-provisioned server capacity is transitioned to low-power states and they remain in those states for as long as the performance remains within given acceptable thresholds. The platform power expenditure is minimized subject to performance constraints. This is formulated as a performance-per-watt optimization problem and solved using analytical power and performance models. Coarse-grained optimizations at the platform-level are refined by local optimizations at the devices-level namely - the processor & memory subsystems. Our adaptive interleaving technique for memory power management yielded about 48.8% (26.7 kJ) energy savings compared to traditional techniques measured at 4.5%. Our adaptive platform power and performance management technique demonstrated 56.25% energy savings for memory-intensive workload, 63.75% savings for processor-intensive workload and 47.5% savings for a mixed workload while maintaining platform performance within given acceptable thresholds.

A measurement-based reduced-order heat transfer modeling framework is developed to optimize cooling costs of dynamic and virtualized data centers. The reduced-order model is based on a proper orthogonal decomposition-based model order reduction technique. For data center heat transfer modeling, the framework simulates air temperatures and CPU temperatures as a parametric response surface with different cooling infrastructure design variables as the input parameters. The parametric framework enables an efficient design optimization tool and is used to solve several important problems related to energy-efficient thermal design of data centers. The first of these problems is about determining optimal response time during emergencies such as power outages in data centers. To solve this problem, transient air temperatures are modeled with time as a parameter. This parametric prediction framework is useful as a near-real-time thermal prognostic tool. The second problem pertains to reducing temperature monitoring cost in data centers. To solve this problem, transient air temperatures are modeled with spatial location as the parameter. This parametric model improves spatial resolution of measured temperature data and thereby reduces sensor requisition for transient temperature monitoring in data centers. The third problem is related to determining optimal cooling set points in response to dynamically-evolving heat loads in a data center. To solve this problem, transient air temperatures are modeled with heat load and time as the parameters. This modeling framework is particularly suitable for life-cycle design of data center cooling infrastructure. The last problem is related to determining optimal cooling set points in response to dynamically-evolving computing workload in a virtualized data center. To solve this problem, transient CPU temperatures under a given computing load profile are modeled with cooling resource set-points as the parameters.

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A central task in computer vision is detecting object classes such as cars and horses in complex scenes. Training an object class detector typically requires a large set of images labeled with tight bounding boxes around every object instance. Obtaining such data requires human annotation, which is very expensive and time consuming. Alternatively, researchers have tried to train models in a weakly supervised setting (i.e., given only image-level labels), which is much cheaper but leads to weaker detectors. In this thesis, we propose new and efficient human annotation schemes for training object class detectors that bypass the need for drawing bounding boxes and reduce the annotation cost while still obtaining high quality object detectors. First, we propose to train object class detectors from eye tracking data. Instead of drawing tight bounding boxes, the annotators only need to look at the image and find the target object. We track the eye movements of annotators while they perform this visual search task and we propose a technique for deriving object bounding boxes from these eye fixations. To validate our idea, we augment an existing object detection dataset with eye tracking data. Second, we propose a scheme for training object class detectors, which only requires annotators to verify bounding-boxes produced automatically by the learning algorithm. Our scheme introduces human verification as a new step into a standard weakly supervised framework which typically iterates between re-training object detectors and re-localizing objects in the training images. We use the verification signal to improve both re-training and re-localization. Third, we propose another scheme where annotators are asked to click on the center of an imaginary bounding box, which tightly encloses the object. We then incorporate these clicks into a weakly supervised object localization technique, to jointly localize object bounding boxes over all training images. Both our center-clicking and human verification schemes deliver detectors performing almost as well as those trained in a fully supervised setting. Finally, we propose extreme clicking. We ask the annotator to click on four physical points on the object: the top, bottom, left- and right-most points. This task is more natural than the traditional way of drawing boxes and these points are easy to find. Our experiments show that annotating objects with extreme clicking is 5 X faster than the traditional way of drawing boxes and it leads to boxes of the same quality as the original ground-truth drawn the traditional way. Moreover, we use the resulting extreme points to obtain more accurate segmentations than those derived from bounding boxes.

The purpose of this master thesis is to investigate the usage of the Lattice Boltzmann Method (LBM) of Computational Fluid Dynamics (CFD) for real-time prediction of indoor air flows inside a data center module. Thermal prediction is useful in data centers for evaluating the placement of heat-generating equipment and air conditioning. To perform the simulation a program called RAFSINE was used, written by Nicholas Delbosc at the University of Leeds, which implemented LBM on Graphics Processing Units (GPUs) using NVIDIA CUDA. The program used the LBM model called Bhatnagar-Gross-Krook (BGK) on a 3D lattice and had the capability of executing thermal simulations in real-time or faster than real-time. This fast rate of execution means a future application for this simulation could be as a predictive input for automated air conditioning control systems, or for fast generation of training data sets for automatic fault detection systems using machine learning. In order to use the LBM CFD program even from hardware not equipped with NVIDIA GPUs it was deployed on a remote networked server accessed through Virtual Network Computing (VNC). Since RAFSINE featured interactive OpenGL based 3D visualization of thermal evolution, accessing it through VNC required use of the VirtualGL toolkit which allowed fast streaming of visualization data over the network. A simulation model was developed describing the geometry, temperatures and air flows of an experimental data center module at RISE SICS North in Luleå, Sweden, based on measurements and equipment specifications. It was then validated by comparing it with temperatures recorded from sensors mounted in the data center. The thermal prediction was found to be accurate on a room-level within ±1° C when measured as the average temperature of the air returning to the cooling units, with a maximum error of ±2° C on an individual basis. Accuracy at the front of the server racks varied depending on the height above the floor, with the lowest points having an average accuracy of ±1° C, while the middle and topmost points had an accuracy of ±2° C and ±4° C respectively. While the model had a higher error rate than the ±0.5° C accuracy of the experimental measurements, further improvements could allow it to be used as a testing ground for air conditioning control or automatic fault detection systems.

Made available in DSpace on 2014-06-12T15:55:22Z (GMT). No. of bitstreams: 2 arquivo2234_1.pdf: 7802626 bytes, checksum: 1cb84477d378fe2e57a785c1609ceac9 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Aspectos de sustentabilidade têm recebido grande atenção pela comunidade científica, devido às preocupações com a satisfação das necessidades atuais de energia sem comprometer, por exemplo, recursos não-renováveis para as gerações futuras. Na verdade, uma crescente demanda de energia é uma questão que tem impactado a forma de como os sistemas são concebidos (data centers, por exemplo), no sentido de que os projetistas necessitam verificar vários trade-offs e selecionar uma solução viável considerando a utilização da energia e outras métricas, tais como confiabilidade e dispobilidadade. As ferramentas são importantes neste contexto para automatizar várias atividades de projeto e obter resultados o mais rápido possível. Este trabalho apresenta um ambiente integrado, denominado, ASTRO, que contempla: (i) Diagramas de Blocos de Confiabilidade (RBD) e Redes de Petri Estocásticas (SPN) para avaliação de dependabilidade , (ii) um método baseado na avaliação do ciclo de vida (LCA) para a quantificação do impacto da sustentabilidade. ASTRO foi concebido para avaliar infra-estruturas de centros de dados, mas o ambiente é genérico o suficiente para avaliar sistemas em geral. Além disso, um estudo de caso é fornecido para demonstrar a viabilidade do ambiente proposto

Made available in DSpace on 2014-06-12T16:00:41Z (GMT). No. of bitstreams: 2 arquivo6891_1.pdf: 3592402 bytes, checksum: ef946f0de5820fb05efe2612994a5ced (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Nos dias atuais, questões relacionadas `a dependabilidade, como alta disponibilidade e alta confiabilidade estão cada vez mais em foco, principalmente devido aos serviços baseados na internet, que normalmente requerem operação ininterrupta dos serviços. Para melhorar os sistemas (arquiteturas de data center, por exemplo), deve ser realizada análise de dependabilidade. As atividades de melhoria normalmente envolvem redundância de componentes o que exige ainda, análise dos componentes. A fim de obter os valores de dependabilidade, além de entender as funcionalidades dos componentes, é importante quantificar a importância de cada componente para o sistema, além da relação entre dependabilidade e custos. Neste contexto, é importante o auxílio de ferramentas que automatizem atividades do projeto, reduzindo o tempo para se obter os resultados, quando comparados ao processo manual. Este trabalho propõe novos índices para quantificar a importância de componentes, relacionando custos, que podem auxiliar projetistas de data center. Adicionalmente, extensões para a ferramenta ASTRO (núcleo Mercury) foram implementadas e representam um dos resultados deste trabalho. Estas extensões incluem avaliação de importância de componentes, avaliação de dependabilidade por limites e geração das funções lógica e estrutural. Além disso, foram implementadas melhorias no módulo de Diagramas de Blocos para Confiabilidade (RBD). Mercury permite a análise de dependabilidade através de Redes de Petri Estocásticas (SPN) e RBD. Ainda considerando a ferramenta ASTRO, é possível quantificar o impacto na sustentabilidade de infraestruturas de data center. Todas as métricas implementadas foram avaliadas em arquiteturas de data center, embora não sejam limitadas a estas estruturas, podendo ser utilizadas para avaliar sistemas em geral. Para demonstrar a aplicabilidade deste trabalho foram gerados três estudos de caso

In today’s scenario, energy efficiency has become one of the most crucial issues for Data Center Networks (DCN). This paper analyses the energy saving capability of a Data center network using Segment Routing (SR) based model within a Software Defined Network (SDN) architecture. Energy efficiency is measured in terms of number of links turned off and for how long the links remain in sleep mode. Apart from saving the energy by turning off links, our work further efficiently manages the traffic within the available links by using Per-packet based load balancing approach. Aiming to avoid congestion within DCN’s and increase the sleeping time of inactive links. An algorithm for deciding the particular set of links to be turned off within a network is presented. With the introduction of per-packet approach within SR/SDN model, we have successfully saved 21 % of energy within DCN topology. Results show that the proposed Per-packet SR model using Random Packet Spraying (RPS) saves more energy and provides better performance as compared to Per-flow based SR model, which uses Equal Cost Multiple Path (ECMP) for load balancing. But, certain problems also come into picture using per-packet approach, like out of order packets and longer end to end delay. To further solidify the effect of SR in saving energy within DCN and avoid previously introduced problems, we have used per-flow based Flow Reservation approach along with a proposed Flow Scheduling Algorithm. Flow rate of all incoming flows can be deduced using Flow reservation approach, which is further used by Flow Scheduling Algorithm to increase Bandwidth utilization Ratio of links. Ultimately, managing the traffic more efficiently and increasing the sleeping time of links, leading to more energy savings. Results show that, the energy savings are almost similar in per-packet based approach and per-flow based approach with bandwidth reservation. Except, the average sleeping time of links in per-flow based approach with bandwidth reservation decreases less severely as compared to per-packet based approach, as overall traffic load increases.

This diploma thesis is focused on the security of cloud computing. Theoretical part of this thesis identifies options which generally lead to higher safety and availability of applications, regardless of the solution. A package of cloud services (called Productivity Suite) was defined, based on the customers' requirements, which is built on Microsoft platform and combined of the following products: Microsoft Exchange, Microsoft SharePoint, Microsoft Lync and is intended to be used by end customers. Specification of the service package, identified opportunities for strengthening the level of security and requirements of potential customers are the primary inputs for designing safe cloud infrastructure, which is the main contribution of this thesis. First step of designing infrastructure is to choose the service provider of data center, who will operate the solution. A decision must be made to select leased solution or owned components. The result of this part is a calculation, which contains all HW components (servers, firewalls, switches, tape library backups, disk arrays) and SW components considering the licensing policy, SSL certificate, domain, backup solution and other operating costs. The solution is limited by financial resources. The priority is safety, security and quality of services.