HPS-HDS:High Performance Scheduling for Heterogeneous Distributed Systems

Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, data-aware service allocation, Big Data management and processing, performance analysis and optimization

Robust Scheduling in Cloud Environment Based on Heuristic Optimization Algorithm

Aiming at analyzing performance in cloud computing, some unpredictable perturbations which may lead to performance downgrade are essential factors that should not be neglected. To avoid performance downgrade in cloud computing system, it is reasonable to measure the impact of the perturbations, and further propose a robust scheduling strategy to maintain the performance of the system at an acceptable level. In this paper, we first describe the supply-demand relationship of service between cloud service providers and customers, in which the profit and waiting time are objectives they most concerned. Then, on the basis of introducing the lowest acceptable profit and longest acceptable waiting time for cloud service providers and customers respectively, we define a robustness metric method to declare that the number and speed of servers should be adequately configured in a feasible region, such that the performance of cloud computing system can stay at an acceptable level when it is subject to the perturbations. Subsequently, we discuss the robustness metric method in several cases, and propose heuristic optimization algorithm to enhance the robustness of the system as much as possible. At last, the performances of the proposed algorithm are validated by comparing with DE and PSO algorithm, the results show the superiority of the proposed algorithm

Game theory in solving conflicts on local government level

Game theory is often used while making decisions in situations of conflicted interests. This research studies practical application of game theory with emphasis on applying linear programming, i.e. simplex algorithm while solving problems in game theory domain. Different cases of game theory application in various scientific disciplines went through theoretical analysis. After the analysis was conducted, this paper presented a practical example of solving a situation of conflict on local government level and the efficiency of game theory while making decision

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Nuevos algoritmos de soft-computing en física atmosférica

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 12-03-2019This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...En esta Tesis Doctoral se elaboran y analizan en detalle diferentes algoritmos híbridos deSoft-Computing para problemas de optimización y predicción en Física de la Atmósfera. El núcleo central de la Tesis es un algoritmo meta-heurístico de optimización recientemente desarrollado, conocido como Coral Reefs Optimization algorithm (CRO). Este algoritmo pertenece a la familia de la Computación Evolutiva, de forma que considera una población de solucionesa un problema concreto, y simula los diferentes procesos que ocurren en un arrecife de coralpara evolucionar dicha población hacia la solución óptima del problema. Recientemente se han propuesto diferentes versiones del algoritmo CRO básico para obtener mecanismos potentes de optimización co-evolutiva. Una de estas modificaciones es el CRO-SL, en la que se definen un conjunto de Sustratos en el algoritmo, de manera que cada sustrato simula un mecanismo de evolución diferente, que son aplicados a la vez en una única población. Otra modificación hadado lugar al conocido como CRO-SP, un algoritmo donde se definen diferentes Especies, capaz de manejar varias codificaciones para un mismo problema a la vez. Estas versiones del CRO han sido hibridadas con varias técnicas de Aprendizaje Máquina, tales como varios tipos de redes neuronales de entrenamiento rápido, sistemas de aprendizaje tales como Máquinas de Vectores Soporte, o sistemas de predicción vinculados totalmente al área de la Física Atmosférica, tales como el Método de los Análogos (AM). Los algoritmos híbridos obtenidos son muy robustos y capaces de obtener excelentes soluciones en diferentes problemas donde han sido probados...Fac. de Ciencias FísicasTRUEunpu

Innovations in the Food System: Exploring the Future of Food

Innovations in Food Systems should be: Inclusive: ensuring economic and social inclusion for all food system actors, especially smallholders, women, and youth; Sustainable: minimizing negative environmental impacts, conserving scarce natural resources, and strengthening resiliency against future shocks; Efficient: producing adequate quantities of food for global needs while minimizing postharvest loss and consumer waste; Nutritious and healthy: enabling the consumption of a diverse range of healthy, nutritious, and safe foods. These are ambitious goals that will require multidisciplinary effort—from engineering to life sciences, biotechnology, medical sciences, social sciences, and economic sciences. New technologies and scientific discoveries are the solutions to the increasing demand for sufficient, safe, healthy, and sustainable foods influenced by the increased public awareness of their importance

Ocean Solutions, Earth Solutions

What affects the oceans affects terra firma. Ocean Solutions, Earth Solutions gathers the insights of more than 50 ocean and coastal science researchers exploring ocean components and their relationships, patterns, and trends over time and space. The book's 16 chapters feature geographic information system (GIS) best practices and include additional online resources.The book is edited by oceanographer and Esri Chief Scientist Dawn J. Wright and features a foreword by oceanographer David Gallo, director of special projects for the Woods Hole Oceanographic Institution in Massachusetts.Keywords: marine resource management, GIS, mapping, ocean conservation, ocean scienc

Optimización de problemas de distribución en planta mediante algoritmos evolutivos

Este trabajo de investigación acomete el problema de distribución en planta. De forma resumida, este problema comprende la distribución de los diferentes departamentos que integran una planta industrial de la forma más satisfactoria posible teniendo en cuenta ciertos criterios y restricciones. Dependiendo de las características del problema, pueden originarse multitud de taxonomías o subproblemas de distribución en planta. En esta tesis doctoral, se abordará el problema de distribución en planta de áreas desiguales que ha sido uno de los más estudiados. Para resolver este problema de distribución en planta de áreas desiguales (UAFLP en inglés), han sido utilizadas multitud de propuestas con el objetivo de obtener el diseño más satisfactorio de la planta industrial. En este sentido, los algoritmos evolutivos han sido ampliamente utilizados en la bibliografía. Por otro lado, dentro de los posibles criterios a considerar cuando se resuelve el problema de distribución en planta, el coste de flujo de material ha sido el más empleado, ya que está directamente relacionado con el coste total de una planta industrial. Es por esta razón que esta tesis doctoral pretende resolver el problema de distribución en planta teniendo en cuenta el criterio del coste de flujo de material, con el objetivo de obtener mejores soluciones que las existentes hasta el momento en la bibliografía de referencia. Para ello, se ha empleado una novedosa y reciente metaheurística que se basa en el comportamiento existente en los arrecifes de corales marinos. Esta nueva metaheurística ha sido empleada con mucho éxito en diferentes problemas complejos de optimización, logrando obtener unos resultados muy satisfactorios en diferentes ámbitos y áreas. Este algoritmo de optimización basado en algoritmos de arrecifes de coral ha sido aplicado al problema de distribución en planta de áreas desiguales considerando el coste de flujo de material como criterio de optimización. La aplicación de esta propuesta es una contribución totalmente original al problema de distribución en planta, ya que, hasta el momento no había sido probado en este campo. La propuesta de optimización basada en los arrecifes de coral ha sido probada de forma empírica con multitud de problemas de referencia de la bibliografía de diferente complejidad. Como resultado se ha mejorado las soluciones existentes hasta el momento en la mayoría de los casos probados. Por otro lado, con el objetivo de dar más diversidad a la población y para evitar que el algoritmo caiga en óptimos locales, se ha propuesto una mejora sobre esta metaheurística que se basa en un modelo de islas de arrecifes de coral, lo que permite realizar una paralelización del algoritmo inicial y así, evolucionar diferentes poblaciones de arrecifes de coral al mismo tiempo. Se ha realizado una experimentación empírica con multitud de problemas de referencia de la bibliografía que ha permitido validar este nuevo enfoque bioinspirado, ofreciendo como resultado mejoras sobre las soluciones existentes hasta el momento en referencia a la mayoría de los casos probados (incluso mejores soluciones que las obtenidas por la propuesta inicial de algoritmo de arrecifes de coral). Mediante este nuevo modelo de islas de arrecifes de coral, se consigue también aumentar la diversidad de las soluciones del problema, lo que permite encontrar nuevas soluciones con mejores aptitudes en términos de coste de flujo de material y en menor tiempo de cómputo. Este nuevo modelo de islas de arrecifes de coral, es una nueva metaheurística que ha sido creada en esta investigación y es totalmente original. Ya que hasta ese momento, no existía ninguna propuesta paralelizada del algoritmo de optimización basado en arrecifes de coral. Por lo que, este nuevo modelo ha contribuido de una manera muy considerable en el estado del arte del problema de distribución en planta de áreas desiguales y también en el ámbito de la computación evolutiva y las metaheurísticas.This research work tackles the facility layout problem, in summary, this problem includes the distribution of the different departments that make up an industrial plant in the most satisfactory way possible, taking into account certain criteria y restrictions. Depending on the characteristics of the problem, a multitude of facility layout taxonomies or subproblems can arise. In this doctoral thesis, the unequal area facility layout problem is addressed, which has been one of the most studied in the related references. To solve the unequal area facility layout problem (UAFLP), many proposals have been used to obtain the most satisfactory design of the industrial plant. In this sense, evolutionary algorithms have been the most used in the literature. On the other hand, among the possible criteria to consider when solving the unequal area facility layout problem, the cost of material flow has been the most employed, since it is directly related to the total cost of an industrial plant. This is the reason why this doctoral thesis aims to solve the unequal area facility layout problem taking into account the criterion of the cost of material flow, intending to obtain better solutions than the consequences so far in the reference bibliography. For this, a new y recent metaheuristic has been used that is based on the behaviour existing in the marine coral reefs. This new metaheuristic has been used with great success in different complex optimization problems, achieving very satisfactory results in different fields y areas. This optimization algorithm based on coral algorithms has been applied to the unequal area facility layout problem by considering the cost of material flow as an optimization criterion. The application of this proposal is a totally original contribution to the facility layout problem, since, until now, it had not been tested in this field. The optimization proposal based on coral reefs has been empirically tested with a multitude of bibliographic reference problems of different complexity. As a result, the solutions improved so far have been improved in the references in most of the cases tested. Finally, to give more diversity to the population y to avoid the algorithm falling into local optimums, an improvement has been proposed on this metaheuristic that is based on a model of coral reef islands, which allows parallelization of the initial algorithm y thus, evolve different coral reef populations at the same time. Empirical experimentation with a multitude of bibliographic benchmark problems was carried out to validate this new bioinspired approach, y it has resulted in improvements over the solutions that have existed so far in the references in the majority of cases tested (even better solutions than ones obtained by the initial proposal of the coral reefs optimization algorithm). Through this new model of coral reef islands, it is also possible to increase the diversity of the solutions to the problem, allowing to find new designs with better skills in terms of material flow cost y in less computing time. This new island model of coral reef is a new metaheuristic that has been created in this research y is totally original. Since until then, there was no parallelized proposal for the coral reef-based optimization algorithm. Therefore, this new island model has contributed in a very considerable way in the state of the art of the unequal area facility layout problem, and also, in the evolutionary computation and metaheuristics

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and