<p>Cloud computing appears to be a paradigm shift in service oriented computing. Massively scalable Cloud architectures are spawned by new business and social applications as well as Internet driven economics. Besides being inherently large scale and highly distributed, Cloud systems are almost always virtualized and operate in automated shared environments. The deployed Cloud services are still in their infancy and a variety of research challenges need to be addressed to predict their long-term behavior. Performance and dependability of Cloud services are in general stochastic in nature and they are affected by a large number of factors, e.g., nature of workload and faultload, infrastructure characteristics and management policies. As a result, developing scalable and predictive analytics for Cloud becomes difficult and non-trivial. This dissertation presents the research framework needed to develop high fidelity stochastic models for large scale enterprise systems using Cloud computing as an example. Throughout the dissertation, we show how the developed models are used for: (i) performance and availability analysis, (ii) understanding of power-performance trade-offs, (ii) resiliency quantification, (iv) cost analysis and capacity planning, and (v) risk analysis of Cloud services. In general, the models and approaches presented in this thesis can be useful to a Cloud service provider for planning, forecasting, bottleneck detection, what-if analysis or overall optimization during design, development, testing and operational phases of a Cloud.</p> / Dissertation

Diss. (sammanfattning) Stockholm : Karol. inst., 2006. / Härtill 5 uppsatser.

Thesis (Ph. D.)--University of Texas Health Science Center at Houston, School of Public Health, 2007. / Includes bibliographical references (leaves 58-59).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 164-170).

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 155-162).

Dans cette thèse nous étudions deux types de pavages : des pavages par une paire de carres et des pavages sur le réseau tri-hexagonal (Kagome). Nous considérons différents problèmes combinatoires et probabilistes. Nous commençons par le cas des carres 1x1 et 2x2 sur des bandes infinies de hauteur k et obtenons des résultats sur la proportion moyenne des carres 1x1 pour les cas planaire et cylindrique pour k < 11. Nous considérons également des questions échantillonnage et comptage approximatif. Pour obtenir un échantillon aléatoire nous définissons des chaines de Markov pour les pavages par des carres et sur le réseau Kagome. Nous montrons des bornes polynomiales pour le temps de mélange pour les pavages par des carres 1x1 et sxs des régions n log net les pavages Kagome des régions en forme de losange. Nous considérons aussi des chaines de Markov avec des poids w sur les tuiles. Nous montrons le mélange rapide avec des conditions spécifiques sur w pour les pavages par des carres 1x1 et sxs et pavages Kagome. Nous présentons des simulations qui suggèrent plusieurs conjectures, notamment l'existence des régions gelées pour les pavages aléatoires par des carres et sur le réseau Kagome des régions avec des bords non plats. / In this thesis we study two types of tilings : tilings by a pair of squares and tilings on the tri-hexagonal (Kagome) lattice. We consider different combinatorial and probabilistic problems. First, we study the case of 1x1 and 2x2 squares on infinite stripes of height k and get combinatorial results on proportions of 1x1 squares for k < 11 in plain and cylindrical cases. We generalize the problem for bigger squares. We consider questions about sampling and approximate counting. In order to get a random sample, we define Markov chains for square and Kagome tilings. We show ergodicity and find polynomial bounds on the mixing time for nxlog n regions in the case of tilings by 1x1 and sxs squares and for lozenge regions in the case of restrained Kagome tilings. We also consider weighted Markov chains where weights are put on the tiles. We show rapid mixing with conditions on for square tilings by 1x1 and sxs squares and for Kagome tilings. We provide simulations that suggest different conjectures, one of which existence of frozen regions in random tilings by squares and on the Kagome lattice of regions with non flat boundaries.

In this paper we study data on discrete labor market transitions from Austria. In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe - over a period of 40 quarters - whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we apply a new method of Bayesian Markov chain clustering analysis based on inhomogeneous first order Markov transition processes with time-varying transition matrices. In addition, a mixtureof- experts approach allows us to model the probability of belonging to a certain cluster as depending on a set of covariates via a multinomial logit model. Our cluster analysis identifies five career patterns after plant closure and reveals that some workers cope quite easily with a job loss whereas others suffer large losses over extended periods of time.

Nesta dissertação de mestrado, vamos apresentar uma prova para os grandes desvios para variáveis aleatórias independentes e identicamente distribuídas com todos os momentos finitos e para a medida empírica de cadeias de Markov com espaço de estados finito e tempo discreto. Além disso, abordaremos os teoremas de Sanov e Gärtner-Ellis. / In this master thesis it is presented a proof of the large deviations for independent and identically distributed random variables with all finite moments and for the empirical measure of Markov chains with finite state space and with discrete time. Moreover, we address the theorems of Sanov and of Gartner-Ellis.

Este trabalho divide-se em três partes. Na primeira parte fazemos uma breve descrição de cadeias de Markov a tempo discreto e tempo contínuo. Na segunda parte, seguindo o artigo [5], introduzimos o formalismo termodinâmico no espaço de Bernoulli com símbolos dados em um espaço métrico compacto, generalizando a teoria usual onde o espaço de estados é finito. Após, seguindo o artigo [1], introduziremos uma versão do Operador de Ruelle para cadeias de Markov a tempo contínuo. Ainda, a partir de uma função V que funcionará como uma perturbação, definiremos um operador de Ruelle modificado e, para este operador, mostraremos a existência de uma auto-função e uma auto-medida. / This work is divided in three parts. In the first one, we give a brief description of Markov chains in both discrete time and continuous time. In the second one, following the article [5], we introduce the thermodynamic formalism in the Bernoulli space with symbols in a compact metric space, generalizing the usual theory, where the space of states is finite. Then, following the article [1], we will introduce a version of Ruelle Opemtor for Markov chains in continuous time. Also, using a V function, which will be seen as a perturbation, we will define a modified Ruelle operator and, for this operator, we will show the existence of a eigenfunction and a eigenmeasure.

Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-09-28T12:28:44Z No. of bitstreams: 1 DissMPFte.pdf: 1012457 bytes, checksum: 6124d4a74a53050982226492d8d53133 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-10T19:03:27Z (GMT) No. of bitstreams: 1 DissMPFte.pdf: 1012457 bytes, checksum: 6124d4a74a53050982226492d8d53133 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-10T19:03:35Z (GMT) No. of bitstreams: 1 DissMPFte.pdf: 1012457 bytes, checksum: 6124d4a74a53050982226492d8d53133 (MD5) / Made available in DSpace on 2016-10-10T19:03:44Z (GMT). No. of bitstreams: 1 DissMPFte.pdf: 1012457 bytes, checksum: 6124d4a74a53050982226492d8d53133 (MD5) Previous issue date: 2016-04-06 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Consider a sequence of independent coin flips where we denote the result of any landing for H, if coming up head, or T, otherwise. Create patterns with H's and T's, for example, HHHHH or HTHTH. How many times do we have to land the same coin until one such two patterns happens? For example, let the sequences being THTHHHHH and TTHTTHTHTH. The number of times that we landed the coin until HHHHH and HTHTH happens it was eight and ten times respectively. We can generalize this idea for a finite number of patterns in any nite set. Then, the rst of all interest of this dissertation is to nd the distribution of the waiting time until a member of a nite colection of patterns is observed in a sequence of Markov chains of letters in from finite set. More speci cally the letters in a nite set are generated by Markov chain until one of the patterns in any fi nite set happens. Besides that, we will find the probability of a pattern happen before of all patterns in the same nite set. Finally we will find the generator function of probability of waiting time. / Consideremos uma sequência de lan camentos de moedas em que denotamos o resultado de cada lan çamento por H, se der cara, ou por T, se der coroa. Formemos uma palavra apenas com H's e T's, por exemplo, HHHHH ou HTHTH. Quantas vezes arremessaremos uma mesma moeda at e que uma das duas palavras acima ocorrer á? Por exemplo, dadas as sequências THTHHHHH e TTHTTHTHTH. O n úmero de vezes que arremessamos a moeda at é que HHHHH e HTHTH ocorreram pela primeira vez e oito e dez, respectivamente. Podemos generalizar a ideia acima para um n úmero fi nito de palavras em um alfabeto finito qualquer. Assim, o nosso principal objetivo dessa disserta ção e encontrarmos a distribui ção do tempo de espera at é que um membro de uma cole ção fi nita de palavras seja observado em uma sequência de ensaios de Markov de letras de um alfabeto fi nito. Mais especifi camente, as letras de um alfabeto finito são geradas por uma cadeia de Markov at é que uma das palavras de uma cole ção finita ocorra. Al ém disso encontraremos a probabilidade de que determinada palavra ocorra antes das demais palavras pertencentes a um mesmo conjunto fi nito. Por último encontraremos a fun ção geradora de probabilidade do tempo de espera.