Content: Recent trends show that cloud computing adoption is continuously increasing in every organization. So, demand for the cloud datacenters tremendously increases over a period, resulting in significantly increased resource utilization of the datacenters. In this thesis work, research was carried out on optimizing the energy consumption by using packing of the virtual machines in the datacenter. The CloudSim simulator was used for evaluating bin-packing algorithms and for practical implementation OpenStack cloud computing environment was chosen as the platform for this research.   Objectives:  In this research, our objectives are as follows <ul type="disc">Perform simulation of algorithms in CloudSim simulator. Estimate and compare the energy consumption of different packing algorithms. Design an OpenStack testbed to implement the Bin packing algorithm.   Methods: We use CloudSim simulator to estimate the energy consumption of the First fit, the First fit decreasing, Best fit and Enhanced best-fit algorithms. Design a heuristic model for implementation in the OpenStack environment for optimizing the energy consumption for the physical machines. Server consolidation and live migration are used for the algorithms design in the OpenStack implementation. Our research also extended to the Nova scheduler functionality in an OpenStack environment.   Results: Most of the case the enhanced best-fit algorithm gives the better results. The results are obtained from the default OpenStack VM placement algorithm as well as from the heuristic algorithm developed in this simulation work. The comparison of results indicates that the total energy consumption of the data center is reduced without affecting potential service level agreements.   Conclusions: The research tells that energy consumption of the physical machines can be optimized without compromising the offered service quality. A Python wrapper was developed to implement this model in the OpenStack environment and minimize the energy consumption of the Physical machine by shutdown the unused physical machines. The results indicate that CPU Utilization does not vary much when live migration of the virtual machine is performed.

Os problemas de corte e empacotamento são muito comuns nas indústrias e na logística. Dado um conjunto de N itens com diferentes pesos e um conjunto de M contentores com capacidade C, o problema de empacotamento unidimensional consiste em determinar o menor número de contentores a serem utilizados para alocar todos os itens respeitando a restrição de capacidade dos contentores. Nesse estudo pretende-se resolver o problema com instâncias benchmark da literatura, por meio de sessenta heurísticas diferentes, que são comparadas a quatro limitantes inferiores propostos na literatura com o intuito de avaliar a qualidade da solução heurística. Quatro limitantes inferiores e dez heurísticas construtivas diferentes foram programados em C++ num mesmo ambiente computacional, permitindo sua comparação tanto em termos de qualidade das soluções, quanto em termos dos tempos de processamento. Uma heurística simples de troca de itens entre contentores chamada Diferença-de-Quadrados foi proposta para melhorar as soluções iniciais do problema. A metaheurística simulated annealing foi acionada para melhorar a solução inicial quando o limitante inferior não foi atingido. Os parâmetros dos simulated annealing foram determinados com os dados das instâncias de forma diferente da utilizada na literatura. As combinações entre as dez soluções iniciais, a heurística Diferença-de-Quadrados e o simulated annealing geraram um conjunto de sessenta heurísticas diferentes. Os resultados mostraram que o algoritmo proposto é eficiente para resolver o problema com tempos de processamento adequados a tomada de decisão. / Cutting and packing problems are very common in industries and logistics. Given a set of N items with different weights and a set of M bins with full capacity C, the one-dimensional bin packing problem consists of determining the smallest number of bins capable to allocate all items respecting the capacity constraint of the bins. that impose that the sum of the weights of the items allocated to the bin is less than or equal to their capacity. In this study we intend to solve the problem with benchmark instances of the literature, by means of sixty different heuristics, which are compared to four lower bounds proposed in the literature in order to evaluate the quality of the heuristic solution. Four lower bounds and ten different constructive heuristics were programmed in C++ in the same computational environment, allowing their comparison both in terms of the quality of the solutions and in terms of processing times. A simple heuristic of item exchange between bins called Difference-of-Squares was proposed to improve the initial solutions of the problem. The simulated annealing metaheuristic was triggered to improve the initial solution when the lower bounds was not reached. The parameters of the simulated annealing were determined with the data of the instances differently from that used in the literature. The combinations of the ten initial solutions, the Difference-of-Squares heuristic and the simulated annealing generated a set of sixty different heuristics. The results showed that the proposed algorithm is efficient to solve the problem with adequate processing times for decision making.

Neste trabalho desenvolvemos o estudo da álgebra linear, secções cônicas e aplicações. Apresentamos os conceitos mais importantes da álgebra linear, estudando os espaços vetorias, subespaços vetoriais, matriz de mudança de base, transformações lineares e produto interno. O principal resultado do trabalho é o teorema espectral que fornece ferramentas para se estudar as secções cônicas não elementares, ou seja, aquelas nas quais uma parábola, elipse ou hipérbole são apresentadas com seus eixos não paralelos aos eixos coordenados do plano cartesiano. Uma vez de posse deste teorema é mostrado um processo prático no qual transformamos uma equação ax2 +bxy +cy2 +dx +ey + g = 0 na equação k1 (x\')2 + k2 (y\')2 + (dx1 + ey1) x\' + (dx2 + ey2) y\' + g = 0 sem o termo misto xy, onde após a eliminação deste, podemos deduzir a equação da cônica identificando assim esta curva. Apresentamos exemplos de cônicas com eixos paralelos e não paralelos aos coordenados do plano cartesiano e utilizamos o software geogebra para visualização. Também discutimos algumas aplicações das cônicas como trajetória de corpos celestes (planeta Terra e um cometa), princípio de reflexão da parábola mostrando o porquê das antenas e dos captadores de ondas sonoras serem parabólicos. Demonstramos um teorema que denominei de identificador de uma curva cônica pois com ele é possível classificar a cônica sem realizar o processo prático, apenas para isso identificamos através da equação ax2 +bxy + cy2 +dx + ey +g = 0, quais os valores de a;b e c e feito isto calculamos o discriminante b2 - 4ac, analisamos os sinais e a nulidade, ou seja, se é maior que zero, menor que zero ou igual a zero, assim é possível classificar a cônica. / The paper develops the study of linear algebra, conic sections and applications. I present the most important concepts of linear algebra, studying vector spaces, vector subspaces, base change matrix, linear transformations, internal product. The main result of the work is the spectral theorem, which provides tools to study the non-elementary conic sections, that is, those in which a parabola, ellipse or hyperbola are presented with their axes not parallel to the cartesian planes coordinate axes. Using this theorem we show a practical process in which we transform an equation ax2 +bxy + cy2 +dx +ey +g = 0 into the equation k1 (x\')2 +k2 (y\')2 + (dx1 +ey1) x\' (dx2 + ey2) y\' +g = 0 without the mixed term xy, where after its elimination we can deduce the conic equation thus identifying the curve we are looking for. I present examples of conic with parallel and non-parallel axes to the coordinates of the Cartesian plane and use the geogebra software for visualization. I discuss some applications of the conic as a trajectory of celestial bodies (planet Earth and a comet), principle of reflection of parabola showing why the antennas and sound wave pickups are parabolics. I demonstrate a theorem that I named the identifier of a conic curve, with it it is possible to classify the conic without realizing the practical process only for this. I identify through the equation ax2 +bxy + cy2 +dx + ey + g = 0, what are the values of a;b, and c and, with this done, I compute the discriminant b2 - 4ac and analyze the signs and the nullity, that is, if it is greater than zero, less than zero or equal to zero, therefore is possible to classify the conic.

O problema de empacotamento de itens irregulares com balanceamento da carga é encontrado no carregamento de aviões, caminhões e navios. O objetivo é empacotar itens irregulares utilizando o menor número de recipientes possível de forma que os recipientes estejam balanceados, que os itens não se sobreponham e estejam inteiramente contidos no recipiente. Neste trabalho, propomos três heurísticas bases com três variações cada para o problema com recipientes retangulares e irregulares. As heurísticas utilizam abordagens diferentes para representar os itens e para fazer o balanceamento. Uma das heurísticas utiliza malha para representação dos itens e faz o balanceamento dividindo o recipiente em quadrantes e revezando a alocação dos itens entre eles de forma que o balanceamento é feito de forma indireta. Tal heurística resolve o problema tanto para recipientes retangulares quanto irregulares. A segunda heurística utiliza a representação dos itens por polígonos e impossibilita a sobreposição de itens utilizando a técnica do nofit polygon. A heurística constrói a solução item por item, sem posições fixas e a cada item alocado, os itens são deslocados em direção ao centro de gravidade desejado do recipiente. Esta heurística resolve apenas problemas com recipientes retangulares. A última heurística é uma adaptação da heurística anterior para a resolução do problema com recipientes irregulares, de forma que o problema é resolvido em duas fases. Cada heurística base possui três variações cada, totalizando nove heurísticas. As heurísticas foram comparadas com outro trabalho da literatura e conseguiram melhorar os resultados para nove das dezenove instâncias testadas. / The irregular bin packing problem with load balancing is found in the loading of airplanes, trucks and ships. The aim is to use as few bins as possible to pack all the items so that all bins are balanced, items do not overlap and are fully contained in the bin. In this work, we propose three base heuristics with three variations each for the problem with rectangular and irregular bin. The three heuristics use different approaches to represent the items and to balance the bin. One of the heuristics uses a grid to represent the items and does the balancing by dividing the container into quadrants and alternating the allocation of items between them so that the balancing is done indirectly. Such heuristic solves the problem for both rectangular and irregular bins. The second heuristic uses the representation of items by polygons and uses the nofit polygon technique. The heuristic constructs the solution item by item, with no fixed positions and with each item allocated, the items are shifted towards the desired center of gravity of the bin. This heuristic only solves problems with rectangular bins. The last heuristic is an adaptation of the previous one to solve the problem with irregular bins, so that the problem is solved in two phases. Each base heuristic has three variations, totaling nine heuristics. The heuristics were compared with other work in the literature and managed to improve the results for nine of the nineteen instances tested.

No description available.

<p><em>Software Instrumentation is the process of collecting data during an application’s runtime,</em></p> <p><em>which will help us debug, detect errors and optimize the performance of the binary. The</em></p> <p><em>recent increase in demand for low latency and high throughput systems has introduced new</em></p> <p><em>challenges to the process of Software Instrumentation. Software Instrumentation, especially</em></p> <p><em>dynamic, has a huge impact on systems performance in scenarios where there is no early</em></p> <p><em>knowledge of data to be collected. Naive approaches collect too much or too little</em></p> <p><em>data, negatively impacting the system’s performance.</em></p> <p><em>This thesis investigates the overhead added by reservoir sampling algorithms at different</em></p> <p><em>levels of granularity in real-time instrumentation of distributed software systems. Also, this thesis describes the implementation of sampling techniques and algorithms to reduce the overhead caused by instrumentation.</em></p>

In modern day logistics, small and medium sized consumer goods are often manually handled and packaged in fulfillment centres and warehouses. When time efficiency is prioritised, there exists the risk to pick too big a package and thus waste space which can naturally lead to unwanted consequences such as unnecessary transports being booked and excess package material being used. This in turn leads to cost inefficiency and increased environmental impact. One novel way of tackling this issue is to use algorithms made to solve optimization problems such as theff3D bin packing problem to pre-calculate which package to use when assembling an order. However, as fill rates improve, it may become difficult for warehouse workers to fit all items into the selected package, especially when dealing with a larger number of items. To help with this, modern techniques such as spatial augmented reality can be used to visualize the packing process in real time, showing workers which item to place next, and where. Recent studies have shown improved packing speeds using this approach, however further improvements could potentially optimize the usability even further. In this study, a novel spatial AR application was used in conjunction with a depth camera to not only indicate where to place items, but to progress the instructions automatically only if they were placed correctly. The aid-tool was tested on 26 participants and showed significant improvements by a factor of 3.29 for a fill rate of 90%, slight but insignificant improvements by a factor of 1.18 for a fill rate of 85% and performed worse by a factor of 0.71 for a fill rate of 81% when compared against unassisted packing. The study concluded that the chosen spatial AR tool showed promise for fill rates of 90% and above, but the results should be treated with caution as the sample size and methodological flaws severely limited the scope of the findings. / Inom modern logistik skickas små och medelstora konsumtionsvaror ofta via manuella packflöden i terminaler och lager. När tidseffektivitet prioriteras så finns risken att ett för stort paket väljs vilket leder till tomt extautrymme och därmed oönskade konsekvenser såsom onödiga transporter och överflödigt packmaterial. Detta leder i sin tur till kostnadsineffektivitet och miljöpåverkan. Ett nytt sätt att hantera detta problem är att använda algoritmer som löser optimeringsproblem såsom 3D-lådpackningsproblemet för att på förhand räkna ut vilket paket som ska användas när en order packas. Dock kan det bli svårt för lagerarbetarna att passa in alla varor i det utvalda paketet när fyllnadsgraden blir högre, särskilt när antalet varor är högt. För att förenkla detta kan moderna tekniker så som spatial augmented reality användas för att visualisera packningsprocessen i realtid för packaren, och visa vilken vara som skall placeras härnäst, samt var. Nylig forskning har påvisat förbättrade packningstider med detta tillvägagångssätt, men vidare förbättringar skulle potentiellt kunna optimera användarvänligheten ytterligare. I denna studie utvecklades ett nytt spatialt AR-verktyg som användes i kombination med en djupkamera för att inte enbart visa var varor skulle placeras utan dessutom enbart stega framåt i instruktionerna då de placerats korrekt. Hjälpmedelsverktyget testades på 26 deltagare och visade en signifikant förbättring med en faktor av 3.29 för en 90%:ig fyllnadsgrad, en liten men insignifikant förbättring med faktor 1.18 för en fyllnadsgrad på 85% samt presterade sämre med en faktor 0.71 för en 81%:ig fyllnadsgrad i jämförelse med oassisterad packning. Studien kom fram till att det valda AR-hjälpmedlet visade lovande resultat för fyllnadsgrader inkl. och över 90%, men resultaten bör hanteras med försiktighet då en liten urvalsstorlek och metodologiska brister kraftigt begränsade resultatens styrka.

No / Next-generation 5G networks is the future of information networks and it will experience a tremendous growth in traffic. To meet such traffic demands, there is a necessity to increase the network capacity, which requires the deployment of ultra dense heterogeneous base stations (BSs). Nevertheless, BSs are very expensive and consume a significant amount of energy. Meanwhile, cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages the cloud computing technology where baseband processing is performed in the cloud. In addition, the BS sleeping is considered as a promising solution to conserving the network energy. This paper integrates the cloud technology and the BS sleeping approach. It also proposes an energy-efficient scheme for reducing energy consumption by switching off remote radio heads (RRHs) and idle BBUs using a greedy and first fit decreasing (FFD) bin packing algorithms, respectively. The number of RRHs and BBUs are minimized by matching the right amount of baseband computing load with traffic load. Simulation results demonstrate that the proposed scheme achieves an enhanced energy performance compared to the existing distributed long term evolution advanced (LTE-A) system.

Esta pesquisa trata da aplicação de meta-heurísticas baseadas em busca em vizinhança variável em problemas de operação de transportes. Desta forma, buscou-se encontrar problemas complexos durante a operação de sistemas de transportes, nas grandes cidades, que possam ser resolvidos com a aplicação de meta-heurística baseada em busca em vizinhança variável. Este trabalho aborda dois diferentes problemas de planejamento e operação de transportes. O primeiro problema abordado neste trabalho é o Problema de Programação da Tabela de Horários, de Veículos e de Tripulantes de Ônibus, no qual as viagens que comporão a tabela de horários, os veículos que executarão as viagens e as tripulações que operarão os veículos são alocadas simultaneamente e de maneira integrada. O segundo problema a ser abordado é o problema de distribuição física, o qual envolve o agrupamento e a alocação de entregas a uma frota de veículos visando minimizar o frete total. Uma abordagem para a modelagem matemática deste problema é modelar como um problema de bin-packing, com bins de tamanho variável unidimensional (do inglês Variable Sized Bin-Packing Problem - VSBPP), ou seja, uma generalização do tradicional problema de bin-packing no qual bins (veículos) de diferentes capacidades e custos estão disponíveis para a alocação de um conjunto de objetos (cargas), de modo que o custo total dos bins (veículos) utilizados seja mínimo. A outra abordagem proposta para o problema de distribuição física é modelar o problema como um problema de bin-packing, com bins de tamanho variável bidimensional (do inglês Bidimensional Variable Sized Bin-Packing Problem BiD-VSBPP). Assim sendo, trata-se de uma expansão do problema de bin-packing com bins de tamanho variável unidimensional (VSBPP), no qual bins (veículos) de diferentes capacidades (capacidade volumétrica e capacidade de carga) e custos estão disponíveis para a alocação de um conjunto de objetos (cargas), os quais possuem as dimensões peso e volume, de modo que o custo total dos bins (veículos) utilizados seja mínimo. Durante a realização deste trabalho, foi desenvolvido um programa computacional em C++, o qual implementa a meta-heurística Busca em Vizinhança Variável (VNS) e duas meta-heurísticas baseadas em VNS. São apresentados resultados de experimentos computacionais com dados reais e dados benchmarking. Os resultados obtidos comprovam a eficácia das meta-heurísticas propostas. / This work approaches variable neighborhood search meta-heuristic applicate on transport operation problems. This way, we sought find complex transport operation problems in large cities that can be solved with the variable neighborhood search meta-heuristic application. This work approaches two different transport planning and operation problems. The first problem approached in this paper is the Bus Timetable Vehicle Crew Scheduling Problem, in which timetabling, bus and crew schedules are simultaneously determined in an integrated approach. The second problem to be approached is the physical distribution problem which comprises grouping and assigning deliveries to a heterogeneous fleet of vehicles aiming to minimize the total freight cost. The problem can be mathematical modeled as one-dimensional Variable Sized Bin-Packing Problem (VSBPP), a generalization of the traditional bin-packing problem, in which bins (vehicles) with different sizes and costs are available for the assignment of the objects (deliveries) such that the total cost of the used bins (vehicles) is minimized. Another proposed approach to the problem of physical distribution is model as two dimensional Variable Sized Bin-Packing Problem (BiD-VSBPP). Therefore, it is an expansion of the bin-packing problem with bins variable-length-dimensional (VSBPP), in which bins (vehicle) of different capacity (capacity and load carrying capacity) and costs are available for allocation a set of objects (loads), which have the dimensions weight and volume, so that minimized the total cost of bins (vehicle). In this work, was developed a C++ software implemented, which was implemented a meta-heuristic Variable Neighborhood Search (VNS) and two others meta-heuristics based on VNS. Computational results for real-world problems and benchmarking problems are presented, showing the effectiveness of these proposed meta-heuristics.

Durant cette thèse, nous avons introduit les problèmes de Bin Covering avec Contrainte de Distance (BCCD) et de Bin Packing avec Contrainte de Distance (BPCD), qui trouvent leur application dans les réseaux de grande échelle, tel Internet. L'étude de ces problèmes que nous effectuons dans des espaces métriques quelconques montre qu'il est impossible de travailler dans un tel cadre sans avoir recours à de l'augmentation de ressources, un procédé qui permet d'élaborer des algorithmes construisant des solutions moins contraintes que la solution optimale à laquelle elles sont comparées. En plus de résultats d'approximation intéressants, nous prouvons la difficulté de ces problèmes si ce procédé n'est pas utilisé. Par ailleurs, de nombreux outils ont pour objectif de plonger les grands réseaux qui nous intéressent dans des espaces métriques bien décrits. Nous avons alors étudié nos problèmes dans plusieurs espaces métriques spécifiques, et, en particulier, ceux générés par certains de ces outils, comme Vivaldi et Sequoia. / During this Ph.D we introduced Bin Covering under Distance Constraint (BCCD in French) and Bin Packing under Distance Constraint (BPCD in French). Those two problems find their applications in the context of large scale networks, like Internet. We studied those problems in general metric spaces, and proved that using resource augmentation is mandatory. Resource augmentation allows to build algorithms working on solutions with less constraints than the optimal solution to which it is compared to. We found interesting approximations algorithms using this relaxation, and proved the necessity of this resource augmentation. However many tools are used to embed large networks we are interested in in specific metric spaces. Thus we studied those problems in different specific metric spaces, in particular those generated by the use of Vivaldi and Sequoia, two of those tools.