Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

Modeling Techniques for the High-Resolution Interpretation of Cryo-Electron Microscopy Reconstructions

Essential biological processes are governed by organized, dynamic interactions between multiple biomolecular systems. Complexes are thus formed to enable the biological function and get dissembled as the process is completed. Examples of such processes include the translation of the messenger RNA into protein by the ribosome, the folding of proteins by chaperonins or the entry of viruses in host cells. Understanding these fundamental processes by characterizing the molecular mechanisms that enable then, would allow the (better) design of therapies and drugs. Such molecular mechanisms may be revealed trough the structural elucidation of the biomolecular assemblies at the core of these processes. Various experimental techniques may be applied to investigate the molecular architecture of biomolecular assemblies. High-resolution techniques, such as X-ray crystallography, may solve the atomic structure of the system, but are typically constrained to biomolecules of reduced flexibility and dimensions. In particular, X-ray crystallography requires the sample to form a three dimensional (3D) crystal lattice which is technically di‑cult, if not impossible, to obtain, especially for large, dynamic systems. Often these techniques solve the structure of the different constituent components within the assembly, but encounter difficulties when investigating the entire system. On the other hand, imaging techniques, such as cryo-electron microscopy (cryo-EM), are able to depict large systems in near-native environment, without requiring the formation of crystals. The structures solved by cryo-EM cover a wide range of resolutions, from very low level of detail where only the overall shape of the system is visible, to high-resolution that approach, but not yet reach, atomic level of detail. In this dissertation, several modeling methods are introduced to either integrate cryo-EM datasets with structural data from X-ray crystallography, or to directly interpret the cryo-EM reconstruction. Such computational techniques were developed with the goal of creating an atomic model for the cryo-EM data. The low-resolution reconstructions lack the level of detail to permit a direct atomic interpretation, i.e. one cannot reliably locate the atoms or amino-acid residues within the structure obtained by cryo-EM. Thereby one needs to consider additional information, for example, structural data from other sources such as X-ray crystallography, in order to enable such a high-resolution interpretation. Modeling techniques are thus developed to integrate the structural data from the different biophysical sources, examples including the work described in the manuscript I and II of this dissertation. At intermediate and high-resolution, cryo-EM reconstructions depict consistent 3D folds such as tubular features which in general correspond to alpha-helices. Such features can be annotated and later on used to build the atomic model of the system, see manuscript III as alternative. Three manuscripts are presented as part of the PhD dissertation, each introducing a computational technique that facilitates the interpretation of cryo-EM reconstructions. The first manuscript is an application paper that describes a heuristics to generate the atomic model for the protein envelope of the Rift Valley fever virus. The second manuscript introduces the evolutionary tabu search strategies to enable the integration of multiple component atomic structures with the cryo-EM map of their assembly. Finally, the third manuscript develops further the latter technique and apply it to annotate consistent 3D patterns in intermediate-resolution cryo-EM reconstructions. The first manuscript, titled An assembly model for Rift Valley fever virus, was submitted for publication in the Journal of Molecular Biology. The cryo-EM structure of the Rift Valley fever virus was previously solved at 27Å-resolution by Dr. Freiberg and collaborators. Such reconstruction shows the overall shape of the virus envelope, yet the reduced level of detail prevents the direct atomic interpretation. High-resolution structures are not yet available for the entire virus nor for the two different component glycoproteins that form its envelope. However, homology models may be generated for these glycoproteins based on similar structures that are available at atomic resolutions. The manuscript presents the steps required to identify an atomic model of the entire virus envelope, based on the low-resolution cryo-EM map of the envelope and the homology models of the two glycoproteins. Starting with the results of the exhaustive search to place the two glycoproteins, the model is built iterative by running multiple multi-body refinements to hierarchically generate models for the different regions of the envelope. The generated atomic model is supported by prior knowledge regarding virus biology and contains valuable information about the molecular architecture of the system. It provides the basis for further investigations seeking to reveal different processes in which the virus is involved such as assembly or fusion. The second manuscript was recently published in the of Journal of Structural Biology (doi:10.1016/j.jsb.2009.12.028) under the title Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions. This manuscript introduces the evolutionary tabu search strategies applied to enable a multi-body registration. This technique is a hybrid approach that combines a genetic algorithm with a tabu search strategy to promote the proper exploration of the high-dimensional search space. Similar to the Rift Valley fever virus, it is common that the structure of a large multi-component assembly is available at low-resolution from cryo-EM, while high-resolution structures are solved for the different components but lack for the entire system. Evolutionary tabu search strategies enable the building of an atomic model for the entire system by considering simultaneously the different components. Such registration indirectly introduces spatial constrains as all components need to be placed within the assembly, enabling the proper docked in the low-resolution map of the entire assembly. Along with the method description, the manuscript covers the validation, presenting the benefit of the technique in both synthetic and experimental test cases. Such approach successfully docked multiple components up to resolutions of 40Å. The third manuscript is entitled Evolutionary Bidirectional Expansion for the Annotation of Alpha Helices in Electron Cryo-Microscopy Reconstructions and was submitted for publication in the Journal of Structural Biology. The modeling approach described in this manuscript applies the evolutionary tabu search strategies in combination with the bidirectional expansion to annotate secondary structure elements in intermediate resolution cryo-EM reconstructions. In particular, secondary structure elements such as alpha helices show consistent patterns in cryo-EM data, and are visible as rod-like patterns of high density. The evolutionary tabu search strategy is applied to identify the placement of the different alpha helices, while the bidirectional expansion characterizes their length and curvature. The manuscript presents the validation of the approach at resolutions ranging between 6 and 14Å, a level of detail where alpha helices are visible. Up to resolution of 12 Å, the method measures sensitivities between 70-100% as estimated in experimental test cases, i.e. 70-100% of the alpha-helices were correctly predicted in an automatic manner in the experimental data. The three manuscripts presented in this PhD dissertation cover different computation methods for the integration and interpretation of cryo-EM reconstructions. The methods were developed in the molecular modeling software Sculptor (http://sculptor.biomachina.org) and are available for the scientific community interested in the multi-resolution modeling of cryo-EM data. The work spans a wide range of resolution covering multi-body refinement and registration at low-resolution along with annotation of consistent patterns at high-resolution. Such methods are essential for the modeling of cryo-EM data, and may be applied in other fields where similar spatial problems are encountered, such as medical imaging.

La odisea de un concepto nómada : el desarrollo sustentable en la órbita de la Geografía

Nuevo paradigma para algunos, o simple eslogan político-mediático para otros, el concepto de desarrollo sustentable levanta reacciones contradictorias en el mundo científico. Existen notables diferencias en el acercamiento al desarrollo sustentable entre los representantes de las ciencias naturales y los de las ciencias humanas. El carácter nómada del concepto (Stengers, 1987) de desarrollo sustentable es percibido por ciertos investigadores en geografía como una verdadera tara genética, cuando otros piensan por el contrario que puede servir de palanca para renovar el acercamiento geográfico a los grandes problemas contemporáneos. ¿Para la investigación en geografía, qué postura es conveniente adoptar? ¿El desarrollo sustentable, como concepto nómada, puede convertirse en una herramienta eficaz e innovadora? ¿O bien su incertidumbre semántica nos conduce de manera inexorable hacia un vagabundeo de las ideas? El objetivo central de esta reflexión es intentar situar el concepto de desarrollo sustentable en la órbita del pensamiento geográfico, en una óptica comparativa con disciplinas vecinas y cuestionando a su vez su interés para la investigación en nuestro campo disciplinario. Para ello, es necesario en primer lugar acercarse sin tabúes a los problemas planteados por este concepto: ¿Debe considerarse el nomadismo conceptual del desarrollo sustentable como una especie de pecado original que lo convertiría en un instrumento inoperante para la geografía? En segundo lugar, desmitificando la novedad aparente del concepto, conviene analizar cuáles son sus filiaciones con la geografía y otras disciplinas. Y finalmente, plantear casos concretos de utilización del concepto en la investigación geográfica (ciudad sustentable; bosques sustentables), subrayando sus aportes, pero también poniendo a la luz sus límites.

A new set of relativistic screening constants for the screened hydrogenic model

AnewRelativisticScreenedHydrogenicModel has been developed to calculate atomic data needed to compute the optical and thermodynamic properties of high energy density plasmas. The model is based on anewset of universal screeningconstants, including nlj-splitting that has been obtained by fitting to a large database of ionization potentials and excitation energies. This database was built with energies compiled from the National Institute of Standards and Technology (NIST) database of experimental atomic energy levels, and energies calculated with the Flexible Atomic Code (FAC). The screeningconstants have been computed up to the 5p3/2 subshell using a Genetic Algorithm technique with an objective function designed to minimize both the relative error and the maximum error. To select the best set of screeningconstants some additional physical criteria has been applied, which are based on the reproduction of the filling order of the shells and on obtaining the best ground state configuration. A statistical error analysis has been performed to test the model, which indicated that approximately 88% of the data lie within a ±10% error interval. We validate the model by comparing the results with ionization energies, transition energies, and wave functions computed using sophisticated self-consistent codes and experimental data.

Planar compact array with parasitic elements for MIMO systems

A compact planar array with parasitic elements is studied to be used in MIMO systems. Classical compact arrays suffer from high coupling which makes correlation and matching efficiency to be worse. A proper matching network improves these lacks although its bandwidth is low and may increase the antenna size. The proposed antenna makes use of parasitic elements to improve both correlation and efficiency. A specific software based on MoM has been developed to analyze radiating structures with several feed points. The array is optimized through a Genetic Algorithm to determine parasitic elements position in order to fulfill different figures of merit. The proposed design provides the required correlation and matching efficiency to have a good performance over a significant bandwidth.

Grammar-Guided Evolutionary Construction of Bayesian networks

This paper proposes the EvoBANE system. EvoBANE automatically generates Bayesian networks for solving special-purpose problems. EvoBANE evolves a population of individuals that codify Bayesian networks until it finds near optimal individual that solves a given classification problem. EvoBANE has the flexibility to modify the constraints that condition the solution search space, self-adapting to the specifications of the problem to be solved. The system extends the GGEAS architecture. GGEAS is a general-purpose grammar-guided evolutionary automatic system, whose modular structure favors its application to the automatic construction of intelligent systems. EvoBANE has been applied to two classification benchmark datasets belonging to different application domains, and statistically compared with a genetic algorithm performing the same tasks. Results show that the proposed system performed better, as it manages different complexity constraints in order to find the simplest solution that best solves every problem.

An optimization method for conceptual design in engineering systems based on high order singular value decomposition

Se presenta un nuevo método de diseño conceptual en Ingeniería Aeronáutica basado el uso de modelos reducidos, también llamados modelos sustitutos (‘surrogates’). Los ingredientes de la función objetivo se calculan para cada indiviudo mediante la utilización de modelos sustitutos asociados a las distintas disciplinas técnicas que se construyen mediante definiciones de descomposición en valores singulares de alto orden (HOSVD) e interpolaciones unidimensionales. Estos modelos sustitutos se obtienen a partir de un número limitado de cálculos CFD. Los modelos sustitutos pueden combinarse, bien con un método de optimización global de tipo algoritmo genético, o con un método local de tipo gradiente. El método resultate es flexible a la par que mucho más eficiente, computacionalmente hablando, que los modelos convencionales basados en el cálculo directo de la función objetivo, especialmente si aparecen un gran número de parámetros de diseño y/o de modelado. El método se ilustra considerando una versión simplificada del diseño conceptual de un avión. Abstract An optimization method for conceptual design in Aeronautics is presented that is based on the use of surrogate models. The various ingredients in the target function are calculated for each individual using surrogates of the associated technical disciplines that are constructed via high order singular value decomposition and one dimensional interpolation. These surrogates result from a limited number of CFD calculated snapshots. The surrogates are combined with an optimization method, which can be either a global optimization method such as a genetic algorithm or a local optimization method, such as a gradient-like method. The resulting method is both flexible and much more computationally efficient than the conventional method based on direct calculation of the target function, especially if a large number of free design parameters and/or tunablemodeling parameters are present. The method is illustrated considering a simplified version of the conceptual design of an aircraft empennage.

Non-linear Control of an Autonomous Ground Vehicle

In this paper, in order to select a speed controller for a specific non-linear autonomous ground vehicle, proportional-integral-derivative (PID), Fuzzy, and linear quadratic regulator (LQR) controllers were designed. Here, in order to carry out the tuning of the above controllers, a multicomputer genetic algorithm (MGA) was designed. Then, the results of the MGA were used to parameterize the PID, Fuzzy and LQR controllers and to test them under laboratory conditions. Finally, a comparative analysis of the performance of the three controllers was conducted.

Array Compacto De Monopolos Planos Con Plano De Tierra Ranurado Para Sistemas Mimo

A compact array of monopoles with a slotted ground plane is analyzed for being used in MIMO systems. Compact arrays suffer usually from high coupling which degrades significantly MIMO benefits. Through a matching network, main drawbacks can be solved, although it tends to provide a low bandwidth. The studied design is an array of monopoles with a slot in the ground plane. The slot shape is optimized with a Genetic Algorithm and an own electromagnetic software based on MoM in order to fulfill main figures of merit within a significant bandwidth

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Optimización de la programación(scheduling) en Talleres de Mecanizado

Los problemas de programación de tareas son muy importantes en el mundo actual. Se puede decir que se presentan en todos los fundamentos de la industria moderna, de ahí la importancia de que estos sean óptimos, de forma que se puedan ahorrar recursos que estén asociados al problema. La programación adecuada de trabajos en procesos de manufactura, constituye un importante problema que se plantea dentro de la producción en muchas empresas. El orden en que estos son procesados, no resulta indiferente, sino que determinará algún parámetro de interés, cuyos valores convendrá optimizar en la medida de lo posible. Así podrá verse afectado el coste total de ejecución de los trabajos, el tiempo necesario para concluirlos o el stock de productos en curso que será generado. Esto conduce de forma directa al problema de determinar cuál será el orden más adecuado para llevar a cabo los trabajos con vista a optimizar algunos de los anteriores parámetros u otros similares. Debido a las limitaciones de las técnicas de optimización convencionales, en la presente tesis se presenta una metaheurística basada en un Algoritmo Genético Simple (Simple Genetic Algorithm, SGA), para resolver problemas de programación de tipo flujo general (Job Shop Scheduling, JSS) y flujo regular (Flow Shop Scheduling, FSS), que están presentes en un taller con tecnología de mecanizado con el objetivo de optimizar varias medidas de desempeño en un plan de trabajo. La aportación principal de esta tesis, es un modelo matemático para medir el consumo de energía, como criterio para la optimización, de las máquinas que intervienen en la ejecución de un plan de trabajo. Se propone además, un método para mejorar el rendimiento en la búsqueda de las soluciones encontradas, por parte del Algoritmo Genético Simple, basado en el aprovechamiento del tiempo ocioso. ABSTRACT The scheduling problems are very important in today's world. It can be said to be present in all the basics of modern industry, hence the importance that these are optimal, so that they can save resources that are associated with the problem. The appropriate programming jobs in manufacturing processes is an important problem that arises in production in many companies. The order in which they are processed, it is immaterial, but shall determine a parameter of interest, whose values agree optimize the possible. This may be affected the total cost of execution of work, the time needed to complete them or the stock of work in progress that will be generated. This leads directly to the problem of determining what the most appropriate order to carry out the work in order to maximize some of the above parameters or other similar. Due to the limitations of conventional optimization techniques, in this work present a metaheuristic based on a Simple Genetic Algorithm (Simple Genetic Algorithm, SGA) to solve programming problems overall flow rate (Job Shop Scheduling, JSS) and regular flow (Flow Shop Scheduling, FSS), which are present in a workshop with machining technology in order to optimize various performance measures in a plan. The main contribution of this thesis is a mathematical model to measure the energy consumption as a criterion for the optimization of the machines involved in the implementation of a work plan. It also proposes a method to improve performance in finding the solutions, by the simple genetic algorithm, based on the use of idle time.

Algoritmos de distribución de cargas de proceso en computación con membranas

Con el surgir de los problemas irresolubles de forma eficiente en tiempo polinomial en base al dato de entrada, surge la Computación Natural como alternativa a la computación clásica. En esta disciplina se trata de o bien utilizar la naturaleza como base de cómputo o bien, simular su comportamiento para obtener mejores soluciones a los problemas que los encontrados por la computación clásica. Dentro de la computación natural, y como una representación a nivel celular, surge la Computación con Membranas. La primera abstracción de las membranas que se encuentran en las células, da como resultado los P sistemas de transición. Estos sistemas, que podrían ser implementados en medios biológicos o electrónicos, son la base de estudio de esta Tesis. En primer lugar, se estudian las implementaciones que se han realizado, con el fin de centrarse en las implementaciones distribuidas, que son las que pueden aprovechar las características intrínsecas de paralelismo y no determinismo. Tras un correcto estudio del estado actual de las distintas etapas que engloban a la evolución del sistema, se concluye con que las distribuciones que buscan un equilibrio entre las dos etapas (aplicación y comunicación), son las que mejores resultados presentan. Para definir estas distribuciones, es necesario definir completamente el sistema, y cada una de las partes que influyen en su transición. Además de los trabajos de otros investigadores, y junto a ellos, se realizan variaciones a los proxies y arquitecturas de distribución, para tener completamente definidos el comportamiento dinámico de los P sistemas. A partir del conocimiento estático –configuración inicial– del P sistema, se pueden realizar distribuciones de membranas en los procesadores de un clúster para obtener buenos tiempos de evolución, con el fin de que la computación del P sistema sea realizada en el menor tiempo posible. Para realizar estas distribuciones, hay que tener presente las arquitecturas –o forma de conexión– de los procesadores del clúster. La existencia de 4 arquitecturas, hace que el proceso de distribución sea dependiente de la arquitectura a utilizar, y por tanto, aunque con significativas semejanzas, los algoritmos de distribución deben ser realizados también 4 veces. Aunque los propulsores de las arquitecturas han estudiado el tiempo óptimo de cada arquitectura, la inexistencia de distribuciones para estas arquitecturas ha llevado a que en esta Tesis se probaran las 4, hasta que sea posible determinar que en la práctica, ocurre lo mismo que en los estudios teóricos. Para realizar la distribución, no existe ningún algoritmo determinista que consiga una distribución que satisfaga las necesidades de la arquitectura para cualquier P sistema. Por ello, debido a la complejidad de dicho problema, se propone el uso de metaheurísticas de Computación Natural. En primer lugar, se propone utilizar Algoritmos Genéticos, ya que es posible realizar alguna distribución, y basada en la premisa de que con la evolución, los individuos mejoran, con la evolución de dichos algoritmos, las distribuciones también mejorarán obteniéndose tiempos cercanos al óptimo teórico. Para las arquitecturas que preservan la topología arbórea del P sistema, han sido necesarias realizar nuevas representaciones, y nuevos algoritmos de cruzamiento y mutación. A partir de un estudio más detallado de las membranas y las comunicaciones entre procesadores, se ha comprobado que los tiempos totales que se han utilizado para la distribución pueden ser mejorados e individualizados para cada membrana. Así, se han probado los mismos algoritmos, obteniendo otras distribuciones que mejoran los tiempos. De igual forma, se han planteado el uso de Optimización por Enjambres de Partículas y Evolución Gramatical con reescritura de gramáticas (variante de Evolución Gramatical que se presenta en esta Tesis), para resolver el mismo cometido, obteniendo otro tipo de distribuciones, y pudiendo realizar una comparativa de las arquitecturas. Por último, el uso de estimadores para el tiempo de aplicación y comunicación, y las variaciones en la topología de árbol de membranas que pueden producirse de forma no determinista con la evolución del P sistema, hace que se deba de monitorizar el mismo, y en caso necesario, realizar redistribuciones de membranas en procesadores, para seguir obteniendo tiempos de evolución razonables. Se explica, cómo, cuándo y dónde se deben realizar estas modificaciones y redistribuciones; y cómo es posible realizar este recálculo. Abstract Natural Computing is becoming a useful alternative to classical computational models since it its able to solve, in an efficient way, hard problems in polynomial time. This discipline is based on biological behaviour of living organisms, using nature as a basis of computation or simulating nature behaviour to obtain better solutions to problems solved by the classical computational models. Membrane Computing is a sub discipline of Natural Computing in which only the cellular representation and behaviour of nature is taken into account. Transition P Systems are the first abstract representation of membranes belonging to cells. These systems, which can be implemented in biological organisms or in electronic devices, are the main topic studied in this thesis. Implementations developed in this field so far have been studied, just to focus on distributed implementations. Such distributions are really important since they can exploit the intrinsic parallelism and non-determinism behaviour of living cells, only membranes in this case study. After a detailed survey of the current state of the art of membranes evolution and proposed algorithms, this work concludes that best results are obtained using an equal assignment of communication and rules application inside the Transition P System architecture. In order to define such optimal distribution, it is necessary to fully define the system, and each one of the elements that influence in its transition. Some changes have been made in the work of other authors: load distribution architectures, proxies definition, etc., in order to completely define the dynamic behaviour of the Transition P System. Starting from the static representation –initial configuration– of the Transition P System, distributions of membranes in several physical processors of a cluster is algorithmically done in order to get a better performance of evolution so that the computational complexity of the Transition P System is done in less time as possible. To build these distributions, the cluster architecture –or connection links– must be considered. The existence of 4 architectures, makes that the process of distribution depends on the chosen architecture, and therefore, although with significant similarities, the distribution algorithms must be implemented 4 times. Authors who proposed such architectures have studied the optimal time of each one. The non existence of membrane distributions for these architectures has led us to implement a dynamic distribution for the 4. Simulations performed in this work fix with the theoretical studies. There is not any deterministic algorithm that gets a distribution that meets the needs of the architecture for any Transition P System. Therefore, due to the complexity of the problem, the use of meta-heuristics of Natural Computing is proposed. First, Genetic Algorithm heuristic is proposed since it is possible to make a distribution based on the premise that along with evolution the individuals improve, and with the improvement of these individuals, also distributions enhance, obtaining complexity times close to theoretical optimum time. For architectures that preserve the tree topology of the Transition P System, it has been necessary to make new representations of individuals and new algorithms of crossover and mutation operations. From a more detailed study of the membranes and the communications among processors, it has been proof that the total time used for the distribution can be improved and individualized for each membrane. Thus, the same algorithms have been tested, obtaining other distributions that improve the complexity time. In the same way, using Particle Swarm Optimization and Grammatical Evolution by rewriting grammars (Grammatical Evolution variant presented in this thesis), to solve the same distribution task. New types of distributions have been obtained, and a comparison of such genetic and particle architectures has been done. Finally, the use of estimators for the time of rules application and communication, and variations in tree topology of membranes that can occur in a non-deterministic way with evolution of the Transition P System, has been done to monitor the system, and if necessary, perform a membrane redistribution on processors to obtain reasonable evolution time. How, when and where to make these changes and redistributions, and how it can perform this recalculation, is explained.

Multiobjective optimization applied to structural sizing of low cost university-class microsatellite projects

In recent years, there has been continuing interest in the participation of university research groups in space technology studies by means of their own microsatellites. The involvement in such projects has some inherent challenges, such as limited budget and facilities. Also, due to the fact that the main objective of these projects is for educational purposes, usually there are uncertainties regarding their in orbit mission and scientific payloads at the early phases of the project. On the other hand, there are predetermined limitations for their mass and volume budgets owing to the fact that most of them are launched as an auxiliary payload in which the launch cost is reduced considerably. The satellite structure subsystem is the one which is most affected by the launcher constraints. This can affect different aspects, including dimensions, strength and frequency requirements. In this paper, the main focus is on developing a structural design sizing tool containing not only the primary structures properties as variables but also the system level variables such as payload mass budget and satellite total mass and dimensions. This approach enables the design team to obtain better insight into the design in an extended design envelope. The structural design sizing tool is based on analytical structural design formulas and appropriate assumptions including both static and dynamic models of the satellite. Finally, a Genetic Algorithm (GA) multiobjective optimization is applied to the design space. The result is a Pareto-optimal based on two objectives, minimum satellite total mass and maximum payload mass budget, which gives a useful insight to the design team at the early phases of the design.

Using clustering techniques for intelligent camera-based user interfaces

The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing human-machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must identify properly the action the user wants to perform, so the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work, we propose to model gestures capturing their temporal properties, which significantly reduce storage requirements, and use clustering techniques, namely self-organizing maps and unsupervised genetic algorithm, for their classification. We further propose to train a certain number of algorithms with different parameters and combine their decision using majority voting in order to decrease the false positive rate. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. The testing results demonstrate its high potential.