Modern day networks, both physical and virtual, are designed to support increasingly sophisticated applications based on complex manipulation of information flows. On the flip side, the ever-growing scale of the underlying networks necessitate the use of low-complexity algorithms. Exploring this tension needs an understanding of the relation between these flows and the network structure. In this thesis, we undertake a study of three such processes: aggregation, dissemination and filtering. In each case, we characterize how the network topology imposes limits on these processes, and how one can use knowledge of the topology to design simple yet efficient control algorithms. Aggregation: We study data-aggregation in sensor networks via in-network computation, i.e., via combining packets at intermediate nodes. In particular, we are interested in maximizing the refresh-rate of repeated/streaming aggregation. For a particular class of functions, we characterize the maximum achievable refresh-rate in terms of the underlying graph structure; furthermore we develop optimal algorithms for general networks, and also a simple distributed algorithm for acyclic wired networks. Dissemination: We consider dissemination processes on networks via intrinsic peer-to-peer transmissions aided by external agents: sources with bounded spreading power, but unconstrained by the network. Such a model captures many static (e.g. long-range links) and dynamic/controlled (e.g. mobile nodes, broadcasting) models for long-range dissemination. We explore the effect of external sources for two dissemination models: spreading processes, wherein nodes once infected remain so forever, and epidemic process, in which nodes can recover from the infection. The main takeaways from our results demonstrate: (i) the role of graph structure, and (ii) the power of random strategies. In spreading processes, we show that external agents dramatically reduce the spreading time in networks that are spatially constrained; furthermore random policies are order-wise optimal. In epidemic processes, we show that for causing long-lasting epidemics, external sources must scale with the number of nodes -- however the strategies can be random. Filtering: A common phenomena in modern recommendation systems is the use of user-feedback to infer the 'value' of an item to other users, resulting in an exploration vs. exploitation trade-off. We study this in a simple natural model, where an 'access-graph' constrains which user is allowed to see which item, and the number of items and the number of item-views are of the same order. We want algorithms that recommend relevant content in an online manner (i.e., instantaneously on user arrival). To this end, we consider both finite-population (i.e., with a fixed set of users and items) and infinite-horizon settings (i.e., with user/item arrivals and departures) -- in each case, we design algorithms with guarantees on the competitive ratio for any arbitrary user. Conversely, we also present upper bounds on the competitive ratio, which show that in many settings our algorithms are orderwise optimal. / text

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: minimization, derivatives, correlation, cell migration. Includes bibliographical references (p.52-53).

Master's thesis is focused on implementing modern statistical methods for fitting propability distribution using kernel estimates with regard to the possibilities of their implementation on the PC and the application of specic data sets. Master's thesis is a part of project from MSMT of the Czech Republic no. 1M06047 Center for Quality and Reliability of Production.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: stochastic ordering; Latent class model; Gibbs sampler; balanced scorecard. Includes bibliographical references (p. 64-65).

<p>Storage systems play a vital part in modern IT systems. As the volume of data grows explosively and greater requirement on storage performance and reliability is put forward, effective and efficient design and operation of storage systems become increasingly complicated. </p><p>Such efforts would benefit significantly from the availability of quantitative analysis techniques that facilitate comparison of different system designs and configurations and provide projection of system behavior under potential operational scenarios. The techniques should be able to capture the system details that are relevant to the system measures of interest with adequate accuracy, and they should allow efficient solution so that they can be employed for multiple scenarios and for dynamic system reconfiguration. </p><p>This dissertation develops a set of quantitative analysis methods for modern storage systems using stochastic modeling techniques. The presented models cover several of the most prevalent storage technologies, including RAID, cloud storage and replicated storage, and investigate some major issues in modern storage systems, such as storage capacity planning, provisioning and backup planning. Quantitative investigation on important system measures such as reliability, availability and performance is conducted, and for this purpose a variety of modeling formalisms and solution methods are employed based on the matching of the underlying model assumptions and nature of the system aspects being studied. One of the primary focuses of the model development is on solution efficiency and scalability of the models to large systems. The accuracy of the developed models are validated through extensive simulation.</p> / Dissertation

Indiana University-Purdue University Indianapolis (IUPUI) / Soil moisture is a key variable in dryland ecosystems. Knowing how and to what extent soil moisture is influenced by rainfall and non-rainfall waters (e.g., dew, fog, and water vapor) is essential to understand dryland dynamics. The hyper-arid environment of the Namib Desert with its frequent occurrence of fog events provides an ideal place to conduct research on the rainfall and non-rainfall effects on soil moisture dynamics. Rainfall and soil moisture records was collected from three locations (gravel plain at Gobabeb (GPG), sand dune at Gobabeb (SDG), and gravel plain at Kleinberg (GPK)) within the Namib Desert using CS655 Water Content Reflectometer and tipping-buckets, respectively. The fog data was collected from the FogNet stations. Field observations of rainfall and soil moisture from three study sites suggested that soil moisture dynamics follow rainfall patterns at two gravel plain sites, whereas no significant relationships was observed at the sand dune site. The stochastic modeling results showed that most of soil moisture dynamics can be simulated except the rainless periods. Model sensitivity in response to different soil and vegetation parameters was investigated under diverse soil textures. Sensitivity analyses suggested that soil hygroscopic point (sh), field capacity (sfc) were two main parameters controlling the model output. Despite soil moisture dynamics can be partially explained by rainfall, soil moisture dynamics during rainless period still poorly understood. In addition, characterization of fog distribution in the Namib Desert is still lacking. To this end, nearly two years’ continuous daily records of fog were used to derive fog distribution. The results suggested that fog is able to be well - characterized by a Poisson process with two parameters (arrival rate and average depth). Field observations indicated that there is a moderate positive relationship between soil moisture and fog at GPG and the relationship tend to be less significant at the other two sites. A modified modeling results suggested that mean and general patterns of soil moisture can be captured by the modeling. This thesis is of practical importance for understanding soil moisture dynamics in response to the rainfall and fog changing conditions.

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Apresentamos um modelo estocástico para a evolução de uma espécie pelo processo de seleção natural. Compreender bem o processo evolutivo é de fundamental importância para a biologia, pois é através dele que as espécies e a vida se transformaram ao longo do tempo até chegarmos no mundo como conhecemos hoje. Detalhamos um resultado encontrado na literatura, e também introduzimos algumas variações e sugestões para aprimorar a modelagem original. O modelo proposto é interessante por conta de sua simplicidade e capacidade de capturar aspectos qualitativos esperados segundo as teorias biológicas. / We present a stochastic model for the evolution of a species by natural selection. A good understanding of the evolutionary process is fundamental for the biological sciences, since it describes how life and all species developed until we reached the world as we know today. We show in details a result available on the literature, and also introduce some variations and suggestions in order to improve the original modeling. The model presented here is interesting due to its simplicity and ability to reproduce some qualitative aspects expected from the biological theories.

Apresentamos um modelo estocástico para a evolução de uma espécie pelo processo de seleção natural. Compreender bem o processo evolutivo é de fundamental importância para a biologia, pois é através dele que as espécies e a vida se transformaram ao longo do tempo até chegarmos no mundo como conhecemos hoje. Detalhamos um resultado encontrado na literatura, e também introduzimos algumas variações e sugestões para aprimorar a modelagem original. O modelo proposto é interessante por conta de sua simplicidade e capacidade de capturar aspectos qualitativos esperados segundo as teorias biológicas. / We present a stochastic model for the evolution of a species by natural selection. A good understanding of the evolutionary process is fundamental for the biological sciences, since it describes how life and all species developed until we reached the world as we know today. We show in details a result available on the literature, and also introduce some variations and suggestions in order to improve the original modeling. The model presented here is interesting due to its simplicity and ability to reproduce some qualitative aspects expected from the biological theories.